فهرست مطالب محمدجواد ولدان زوج

Agriculture serves as the cornerstone of the global economy, providing the main source of food and raw materials for various industries. However, the rising demand for food as a consequence of population growth represents a considerable threat to food security, particularly in light of the limited access to freshwater resources. It is noteworthy that agriculture alone consumes about 70% of the world's freshwater resources, thereby emphasizing the critical need to manage and enhance irrigation efficiency to ensure sustainable food production. Therefore, the management and enhancement of irrigation efficiency are essential. At the core of determining irrigation water requirements lies the concept of actual crop evapotranspiration (ETa), which represents the combined water loss from soil evaporation and plant transpiration. Accurate estimation of ETa is crucial in optimizing irrigation methods, maximizing crop yield, and minimizing water consumption. Various models and tools have been developed to estimate ETa, aiming to provide more user-friendly and efficient methods for farmers and researchers. Given the extensive application of ET estimation models, there is a clear need to focus on the development of accurate and efficient methods for determining this parameter. Thus, this study aims to compare user-friendly ETa estimation methods, including the EEFLUX system, the METRICTOOL tool, and the automatic hot and cold pixel selection method of the SEBAL and METRIC models.

The Earth Engine Evapotranspiration Flux (EEFLUX) is a version of the METRIC model that operates on the Google Earth Engine platform. METRICTOOL is a new tool in ArcGIS based on the METRIC model, offering enhanced pre-processing capabilities and automatic data identification. This tool reduces computation time by 50% and provides a user-friendly alternative to other existing METRIC model implementation platforms. The automatic hot and cold pixel selection method involves creating a binary map of eligible pixels using a rule-based classifier and a comprehensive search algorithm to identify hot and cold pixels based on defined criteria. To estimate ET using these methods, six Landsat 8 satellite images were utilized during the winter wheat crop planting period at Tehran University farms in Mohammadshahr Karaj. The evaluation of these methods was conducted using alfalfa reference evapotranspiration (ETr) calculated with the FAO-Penman-Monteith method as reference data.

The Root Mean Square Error (RMSE) values for the EEFLUX system, METRICTOOL, SEBAL, and automatic METRIC tools were determined as 2.45, 0.33, 0.39, and 2.76, respectively. Despite numerical differences, the evaporation and transpiration product of the EEFLUX system showed significant correlations with other methods. For instance, the R2 between ETa estimates from the EEFLUX system and the METRICTOOL tool was found to be 0.91. Although the data from the EEFLUX system may not be precise enough for local studies due to the use of CFSV2 global meteorological data in Iran, they yield acceptable results in large or global-scale studies. The METRICTOOL tool and automatic METRIC model exhibited the highest correlation (R2=0.99) and numerical agreement with each other, with RMSE values of 0.33 and 0.39, respectively, indicating higher accuracy compared to the automatic SEBAL model.

The results of the numerical analysis indicate that the automatic hot and cold pixel selection approach can achieve similar accuracy to that of the METRICTOOL tool. This automated approach enhances the efficiency of the model in terms of time and effectiveness, reducing the potential for human error in estimating evapotranspiration for new or inexperienced users, and making these models accessible to the public. Furthermore, EEFLUX data can be utilised for the implementation of management measures in large-scale studies.

Natural phenomena in the world, such as earthquakes and heavy rains, which are sometimes combined with wind storms, can cause landslides. These landslides in one area can damage several parts and cause significant damage to natural and human infrastructure. Landslides occur in almost all countries of the world and play an important role in the changing of the earth's surface. There are different geodetic and non-geodetic methods to measure the changes caused by this phenomenon. Geodetic methods are not suitable for preparing a landslide damage map due to their limitations such as high cost and time consuming. Therefore, we have to use non-geodetic methods. Nowadays, the use of remote sensing techniques has received much attention. Radar images have been proposed as a suitable tool for monitoring landslides due to their high spatial resolution, wide view, the possibility of capturing in any kind of weather conditions and during the night, the high frequency of spatial and temporal observations, and acceptable accuracy. In addition, there are various methods for extracting information from this type of data, most of which require large initial data, and time-consuming processing. But in this research, we are trying to produce a landslide damage map by using the least input data and in the shortest possible time (no need to spend time for downloading all the required data). Therefore, the method used in this research is the use of Sentinel-1A RADAR images and processing these images in the Google Earth Engine (GEE) processing platform. In this article, we will prepare landslide damage map by examining the changes in the backscattering coefficient image (σ° ) between before and after landslide RADAR images. In this research, by having two sets of images related to before and after the occurrence of the landslide in ASC and DSC pass mode, we can produce the Iratio image between the image before and after the landslide for ASC and DSC mode. After that, we can average between IratioASC and IratioDSC to produce the IratioAverage. After producing this image and removing areas that cause errors and ambiguity, such as seas and lakes, agricultural areas, deforested areas, etc. finally, by determining a suitable threshold, it is possible to detect landslide areas. In order to evaluate the accuracy, since the lack of ground data in the study area, the generated landslide map was compared with Sentinel-2 optical sensor images and the results showed a high agreement between these two data sets, and this shows the high accuracy of the proposed method.

One way to ensure food security is to produce strategic agricultural products on a large scale using industrial methods. Managing large-scale farms consistently and cohesively is a challenging task that requires the utilization of modern technologies. Crop anomalies refer to uncommon and limited factors during agricultural production, leading to localized differentiation in the crop cultivation process. Factors contributing to crop anomalies in agriculture include imbalances in soil nutrients and fertilizers, grazing during crop growth, pests, variations in soil texture and slope in pastures, weed growth, and drought. Detecting and remediating factors limiting crop growth in vast agricultural lands is difficult and these issues are often noticed at harvest time. This article suggests a solution for continuously monitoring of large agricultural fields by analyzing the time series of Sentinel-2 satellite images. The effectiveness of this solution in detecting various anomalies of farms, in agrarian areas has been demonstrated by the results. The proposed solution offers features such as timely diagnosis, the ability to monitor the continuation of irregularities, and the measurement of compensatory measures' effectiveness. The method has successfully identified over five types of anomalies in the selected farms, achieving a detection accuracy of 95.60%.

Due to the wide applications of hyperspectral images, economical and innovative imaging systems are developed to acquire such images. In order to use hyperspectral images, it is necessary to establish an accurate relation between the ground space and the image space, which needs numerous Ground Control Points (GCPs). This fact highlights the need for developing geometric corrections methods for any camera design. BaySpec OCI-F (400-1000 nm) is one of the innovative cameras that acquires pushbroom hyperspectral images. In addition to the pushbroom sensor, the camera uses a frame sensor that acquires images at the same time as the pushbroom sensor and with the same temporal rate. In this article, a geometric correction method for pushbroom images of OCI-F camera is proposed. Based on the camera’s imaging design, the first step of the method determines a set of calibration parameters which geometrically relates the pushbroom and the frame sensors. Then using this relation and the geometric relations among consecutive frames, the pixels of the pushbroom scene are rearranged and form the corrected image. The proposed method determines the relation among the consecutive images via Least Square Matching (LSM) method. The results show that the correction method has decreased the geometric distortions of the raw pushbroom scene by 62.2% on average. Such a reduction causes the average accuracies of two-dimensional and three-dimensional generic models which relate image space and ground space together, to increase by 34.1% and 39.9% respectively.

With increasing the number of large engineering structures in cities, experts are looking for a good solution for monitoring these structures to avoid great financial and human damages. From the past, leveling and ground surveying were carried out to measure the deformation of structures and ground displacements along the vertical direction; but these measurements are time-consuming and costly. Also, the using of precision instruments and deformation sensors are not suitable because of their high cost, time-consuming and complexity. Due to the ability of Radar images and Radar interferometry techniques in the field of monitoring the ground displacement, in this research, we are looking for evaluating the potential of this method for monitoring the dam deformation and displacements. To achieve this goal, we used two sets of radar data which are CosmoSkyMed-X and Sentinel-1A.   In the time series processing of these images, the PSI method was selected then the star graph and Deloney triangulation were used. In the next step, we used both linear and nonparametric models for displacement estimation. The results were evaluated by applying two different displacement models and finally, the model with higher temporal coherence was selected as the appropriate model and the other model was discarded. In processing the Mamlu dam images, the appropriate model for monitoring the displacements with S-1A Radar data was the nonparametric model and for CSK data was linear model. Due to the lack of ground data collection from Mamlu dam in the same period of time with radar data, to evaluate the accuracy and proof the obtained results, the results of two radar data sets (CSK and S-1A) were compared with each other and a very high agreement was observed between the results of these data sets that the amount of RMSE calculated for ASC data of these two sensors is equal to 0.7703 mm and for DSC data was calculated 0.9551 mm, which is a very high consistency of the results, which can be a reason for the accuracy of the extracted results.

Map of croplands is one of the information layers required in the efficient management of these lands. Having such maps makes it possible to monitor agricultural fields during the growing season continuously. In this study, a solution to produce map of Shahrekord’s agricultural lands in two agricultural and non-agricultural classes is presented using the time series of different extracted indices from Sentinel-2 images. Since the use of large data sources is one of the obstacles to the development of methods based on the time series of satellite images, the Google Earth engine processing platform has been used in this study. The proposed method is based on integrating supervised pixel-based classification results with segmentation results. First, training data of supervised classification is provided in a rigorous refining process without the need of collected data from field surveys or interpretation of high-resolution satellite images. Then, by calculating the separability of the two target classes in the time series of each index, the optimal indices are selected. Finally, by combining the results of segmentation and classification methods based on the votes obtained from the classification results, agricultural or non-agricultural class is assigned to each of the image segments. In addition to incorporating spatial information including edges and spatial proximity, this method has been able to improve the noise and porous results of pixel-based classification and has increased the overall accuracy of the final map from 90.7% to 96.05%. Also, user accuracy of both agricultural and non-agricultural classes show an improvement of 3.27 and 7.97%, respectively.

Over the past three decades, with the rapid development of spatial-based satellite imagery, remote sensing technology has found a special place in various applications of urban management. Production of status maps of urban structures, the study of energy loss status, identification of thermal islands, monitoring of urban vegetation, and assessment of air pollution are just a few examples of areas related to urban management that remote sensing technology is the basis for indirect measurement of the related quantities. Maps of urban structures such as building blocks are commonly used in crisis management, urban design, and urban development studies.
 Materials

In this study, the production of urban building block maps using Sentinel 1 and 2 satellite images has been conducted. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Building Index ( NDBI ) for three consecutive months, the slope feature derived from the 30-meter Shuttle Radar Topographic Mission (SRTM)Digital Elevation Model of the study area, along with two Vertical – Vertical (VV) and Vertical - Horizontal ( VH ) polarization in both ascending and descending orbits, form the set of input features.

The proposed method of this paper relies on the use of a generalizable trained classifier. Initially, the classifier is trained in 2015 using training samples obtained from a new rigorous refining process using different remote sensing and spatial products. This rigorous refining process uses a reference urban map of 2015. In the first step, the corresponding areas related to the ways and roads are removed using the OpenStreetMap data layer. Areas suspected of vegetation with NDVI greater than 0.2 are then discarded. Also, due to the high backscattering of buildings in Synthetic Aperture Radar images, areas with a value less than the average backscattering coefficient of the remaining areas are eliminated. Finally, the residual map is refined using the Mahalanabis distance and the Otsu automatic thresholding method. The trained classifier is then used to generate a map of building blocks at similar time intervals for the three target years (2018, 2019, and 2020). Due to the diversity of texture and density of building blocks in the metropolis of Tehran, the proposed method has been evaluated in this area. Due to the concentration of political, welfare, and social facilities, Tehran has experienced more unplanned and irregular expansion and urbanization than other cities in Iran, which has lead to changes in buildings and constructions. Also, due to the availability of free satellite images and various online processing operations, the Google Earth Engine platform has been used in this study. The performance of three different classifiers including Random Forest (RF), Minimum Mahalanabis Distance (MD), and Support Vector Machines (SVM) are examined in this process. In order to evaluate the proposed method, reference samples obtained from visual interpretation of high-resolution satellite images (Google Earth) in all three target years have been used.

The performance of the aforementioned classifiers has been investigated using 3 different criteria: overall accuracy, user accuracy, and F-score of building blocks. The RF method with an overall accuracy of over 93% in all three target years has shown the best performance. The SVM method ranks second with an accuracy of about 91% every three years. However, the MD method with an overall accuracy below 85% in all three target years has not performed well.

The results show better performance of the RF method in all three target years with an overall accuracy of over 93%. It should be noted that the MD classifier with higher user accuracy than other methods, has shown better performance in detecting the class of building blocks. However, the RF method is the best classifier in terms of the user accuracy of the background class. The effect of using two VV and VH polarization and also the slope derived from the SRTM Model in the input feature set on the final accuracy of classification was also investigated. According to the results, the simultaneous use of these three features produces more accurate results in both target classes. However, the results show that the use of VV polarization increases the final classification accuracy compared to VH polarization. The presence of slope feature along with both polarizations has also increased the classification accuracy of each class, especially the background class. However, the exclusion of both VV and VH features from the input feature set has resulted in a more than 10% reduction in overall classification accuracy.

Based on calculated overall accuracies which are above 80% in the majority of investigated cases, two different results can be concluded. First, the trained classifier has shown good temporal generalization and has achieved acceptable accuracy in the target years. Second, due to the different collection processes of training and evaluation data, the proposed rigorous refining method for the preparation of training data has shown good performance. The effect of using two VV and VH polarization and also the slope derived from the SRTM  Digital Elevation Model in the input feature set on the final accuracy of classification was also investigated. According to the results, the simultaneous use of these three features produces more accurate results in both target classes. However, the results show that the use of VV polarization increases the final classification accuracy compared to VH polarization. The presence of slope feature along with both polarizations has also increased the classification accuracy of each class, especially the background class. However, the exclusion of both VV and VH features from the input feature set has resulted in a tangible decreasein overall classification accuracy.

Agriculture has been recognized as the main motive for economic growth and development in different countries of the world. In the meantime, mapping croplands through the classification of remote sensing images is one of the effective solutions in decision making and providing food security to the community. In this research, croplands are classified into different classes of agricultural products (including wheat, barley, corn, alfalfa, potatoes, and Sugar beets) using multi-temporal optical (Sentinel-2) and synthetic aperture radar (Sentinel-1) satellite images. All the steps related to the preparation of satellite images, have been conducted in the Google Earth Engine online processing platform. A novel three-dimensional deep convolutional neural network is used as the classifier. The designed network, in addition to three-dimensional kernels with the ability to extract spatial and temporal information of each pixel simultaneously, uses some escape connections of the previous layers. These connections, contrary to the feed-forward convolutional networks, feed the output of the previous layers to the new layers. After dividing the ground truth data into two categories of training and evaluation and assessing the performance of the network with 50 different training and evaluation data, the network’s overall accuracy was calculated 91.6% on average. According to the final results, the designed escape connections increased the overall accuracy of classification by 2%. The proposed network was also compared with temporal and spatial-temporal Random Forests and Support Vector Machines which showed a better performance with a difference of at least 2.4%.

Todaychr('39')s cities are full of large structures that require very high cost and time to monitor in traditional ways. Existing engineering structures are deformed due to various factors such as landslides, land subsidence, earthquake, flood, explosion and etc. With increasing the number of large engineering structures in cities, experts are looking for a good solution for monitoring these structures to avoid great financial and human damages.  One of the most important huge structures in any country is the dam. In addition to providing drinking water and water for agriculture, dams prevent devastating floods and are able to generate a large amount of hydropower. Therefore, continuous monitoring and study of the displacement and deformation behavior of the dams is essential. From the past, leveling and ground surveying were carried out to measure the deformation of structures and ground displacements along the vertical direction; but these measurements are time-consuming and costly. Also, the use of precision instruments and deformation sensors are not suitable because of their high cost, time-consuming and complex. Due to the ability of Radar images and Radar interferometry techniques in the field of monitoring the ground displacement, in this research, we are looking for evaluating the potential of this method for monitoring the dam deformation and displacements. To achieve this goal, we used two sets of radar data which are CosmoSkyMed-X and Sentinel-1A.   The study area in this study is Daryan earth-fill dam which is located in Kermanshah province. Daryan Dam is a gravel dam with a clay core. The length of the crown of the dam is 368 meters, the width is 15 meters and the height of the foundation is 179 meters. The method used to estimate the displacement of the dam is the Persistent scatterers (PSI) technique. In the time series processing of these images, the PSI method was selected then the star graph and Deloney triangulation were used. In the next step, we used both linear and nonparametric models for displacement estimation. The results were evaluated by applying two different displacement models and finally, the model with higher temporal coherence was selected as the appropriate model and the other model was discarded. In processing the Daryan dam images, the appropriate model for monitoring the displacements with S-1A Radar data was the nonparametric model and for CSK data was linear model. The results of the accuracy assessment showed that by using CSK and S-1A radar data, we can monitor the earth-fill dam displacements by the precision of 2 and 4 mm which these precisions are acceptable and standard. It is important to note that the results of X-band data due to itchr('39')s higher resolution, has a higher density of PS points, and higher accuracy. Also, the larger size or greater area of the dam body makes more the density of the PS points and the displacements obtained for these points will be more accurate. Generally, we can say that the X and C-band radar data have the potential to monitor the earth-fill dam displacements by radar interferometry technique and this method can replace with traditional and costly methods.

In this study, the SAR data is used as a supplementary data to overcome the limitations of the multispectral (MS) image in building detection. Therefore, the proposed method utilizes a multisensor data fusion to take the advantages of both MS and SAR data together. In addition, two different filter-based feature selection methods, MNF and PCA, are investigated as an alternative scenario when the training data is not accessible. In this respect, the optimum feature vector is selected using MNF, PCA and Genetic methods from MS and SAR data, separately. Thereafter, each selected feature vector is used to classify the images by implementing the support vector machine (SVM) and the artificial neural network classification methods. The experimental result shows that the PCA is able to select the feature vector without the need of training data as well as genetic algorithm. However, the MS classification result is poor where both roofs and streets are covered with asphalt. In this framework, the fusion of SAR and MS images in feature level was utilized to improve the classification results. Finally, to assign a label at the sample, a majority voting is calculated between the used classification methods results. However, according to the noisy result, using the neighborhood information in the form of a moving spatial window in different sizes is examined to determine the label of the central pixel more accurately. According to the experimental results, the overall accuracy and building detection accuracy are obtained 92.82% and 80.14%, respectively, which represent the satisfying performance of the proposed method.

Speckle in Synthetic aperture radar images makes grainy effects, because of the coherent imaging system which cause some difficulties in object-oriented processes, like segmentation or classification. Therefore, a lot of methods have been developed for speckle reduction purpose. These methods can be classified but not limited in some approaches, like spatial based, transform based and optimization, which mostly suffer from limitations like edge and texture destruction and also regulating parameter dependence. In this paper a new structure has been presented based on adaptive filtering in the frequency space, which not only reduces the speckle but also preserves edges and delicate textures. In addition, it has low level of computation and complexity compared to the other methods. Proposed method, improves equivalent number of looks index 50 percent and edge preservation index 50 and 30 percent for real and simulated synthetic aperture radar images, respectively.

The spectral Direct Normal Irradiance (SDNI) is a basic radiometric quantity from which many other quantities can be derived. It provides not only information about the spectral and distribution of the direct solar radiation reaching a particular location. Accurate knowledge about the spectral direct irradiance shall enable us to gain new scientific results in the: determination of cloud effects, calculation of power yield of solar cells for electricity providers, derivation of aerosol distributions and their impact on the radiation field, determining the UV spectral irradiance for medical applications and in the improvement of our understanding of the radiative transfer in a changing climate. Nevertheless, at present, DNI measurements are not available in many instances. Therefore, the main objectives of this project are the setup, characterization, the calibration, the validation and the application of an operational measurement for the (SDNI) at the Institute of Meteorology and Climatology (IMUK) at the Leibniz University Hannover. With sampling times of less than one second even rapid changes of the SDNI can be recorded with a spectral resolution of about 1 nm in the wavelength range from 300 -800 nm (from UV to NIR). For this purpose three different tracking units were used to precisely track the sun location in the sky and finally based on the measurements and tracking strategy best instrument was selected. Finally the best tracking unit was used and the set-up was tested in the institute of Meteorology and climathology of Hannover (Longitude 52.37◦, Latitude 9.73◦). The comparison between the integrated value of the measurements and Pyranometer observations on the same platform, and during a complete day presented in this paper. It is worth to mention that the technique for setting up such a measurement station is very important as a basis for remote sensing research focused on the solar energy and meteorology applications.

Forest Canopy Density Mapper is a method based on spectral indexes integration in forest canopy density classification. In this paper, a data integration procedure is used to improve the result. In this respect, SFIM method and spectral response algorithm is utilized without a bad effect on the spectral and radiometric properties of bands. In the following, Landsat images of Hyrcanian forests in the north of Iran were used to implement the conventional and improved methods. Also, the ground measurements including grass-land, thin forest, semi-dense forest and forest is utilized for evaluation. The result shows that the forest canopy density model is inefficient in the thin and semi-dense forests. Alternatively, the results in the dense forest and grass land is reliable. Additionally, the improvement of the proposed method in these two areas is clearly seen. It seems that a high resolution image should be used to improve the accuracy of the forest density classification in the semi-dense and thin forests

In the absence of satellite ephemeris data and inner geometry of satellite’s sensor, utilization of Rational Function Models (RFMs) is one of the best approaches to georeferencing satellite images and extracting spatial information from them. However, since RFMs have high number of coefficients, then usually high number of control points is needed for their estimation. In the other hand, RFM terms are uninterpretable and all of them causes over-parametrization error which count as the most important weakness of the terrain-dependent RFMs. Utilization of optimization algorithms is one of the best approaches to eliminate these weaknesses. Therefore, various optimization algorithms have been used to discover the optimal composition of RFM’s terms. Since the mechanism of these algorithms is different, the performance and feature characteristics of these algorithms differ in the discovery of the optimal composition train-dependent RFM’s terms. But the existing differences not comprehensively analyzed. In this paper, in order to comprehensive assessment the abilities of Genetic Optimization Algorithm (GA), Genetic modified Algorithm (GM), and a modified Particle Swarm Optimization (PSO) in terms of accuracy, quickness, number of control points required, and reliability of results, are evaluated. These methods are evaluated using for different datasets including a GeoEye-1, an IKONOS-2, a SPOT-3-1A, and a SPOT-3-1B satellite images. In terms of accuracy achieved, difference between these methods was less than 0.4 pixel. In terms of speed of evaluation of parameters, GM was 10 to 12 time more quickly in comparison with two other algorithms. In terms of control points required, degree of freedom of modified PSO was 45.25 percent and 27 percent more than GM and GA respectively, and finally in terms of reliability, the dispersion of RMSE obtained in 10 runs of three algorithms are relatively same. These results indicated that accuracy and reliability of all three methods are almost the same, speed of GM is higher and modified PSO needs less control points to optimize terrain-dependent RFM

Automatic roads detection in urban areas is of greater importance and is a persistent research focus in the remote sensing community. The spectral and geometrical varieties of road pixels; their spectral similarity to other features such as buildings, parking lots, and sidewalks; and the occasional obstruction by vehicles and trees are obstacles to the precise identification of urban roads through satellite images. For road detection, panchromatic or multi-spectral images, especially in urban areas, will yield ambiguous results, due to the additional complexities. For example, in an aerial photo or a high-resolution satellite image, both roads and buildings will appear similar, because their construction materials are usually the same. As a result, they cannot be readily distinguished. This becomes worse when they are in shadow or covered by roofs or walls of tall buildings. Accordingly, neither automatic nor semi-automatic methods will be entirely reliable in these dense urban areas. Moreover, the outputs of methods that use 2D images are more ambiguous than those with 3D inputs. Lidar point data have the potential to distinguish 3D features from one another, to distinguish 3D from 2D, and to distinguish 2D features from one another. However, Lidar intensity data are affected by a high amount of noise, and therefore are unable to distinguish roads from features with similar return signal power. Consequently, the full potential of the Lidar data cannot be exploited from raw data. Combining these two kinds of complementary data sources seem to be reasonably promising for road extraction, 3D urban modeling, etc. The main idea behind the integration of Lidar and optical imagery is that the strengths of one data type can compensate for the weaknesses of others. For example, being short of spectral information, Lidar data have high classification confusion between human-made and natural objects, whereas multispectral data have increasing classification confusion between spectrally identical objects in complex urban landscapes. In the light of these findings, in this paper, highresolution QuickBird satellite imagery and Lidar data processed through nearest-neighbor classification based on optimal features have been used together to extract various types of urban roads. This work designed and implemented a ruleoriented strategy based on a masking approach. A supplementary strategy based on optimal design features was also used. Accordingly in the vegetation class, the accuracy was 93% and 93% for the producer and user accuracies respectively. In the case of the high road class, the accuracy 91 % and 84% and in the buildings class, the accuracy was 93% and 93% for the producer and user accuracies, respectively. In the low roads class, the accuracy 89% and 86% and in the open-space class, the accuracy was 80 % and 85% for the producer and user, respectively. Finally the overall precision of class identification is 90 % with a kappa coefficient of 0.87, which shows a satisfactory precision according to different conditions and considerable interclass noise. The final results demonstrate the high capability of object-oriented methods in simultaneous identification of a wide variety of road elements in complex urban areas using both high-resolution satellite imagery and Lidar data.

This paper provides an advanced method to improve results of three stage inversion algorithm using polarimetric synthetic aperture radar interferometry (PolInSAR) technique based on Random Volume over Ground model. In conventional three stage method, the ground phase, extinction coefficient and volume layer is estimated in a geometrical way without the need for a prior information or separate reference DEM. The extinction and volume height estimation is done in the third stage by searching in the two dimension area. In the proposed algorithm, defining a new geometrical index, based on signal penetration in the forest, imposes a limited range for the extinction coefficient. The new index, as an axillary data, help search in a more appropriate space. The proposed algorithm was applied on L-band ESAR single baseline single frequency polarimetric SAR interferometry data. As a result of applying this restriction in the extinction range, a 2.5 meter improvement was observed in the RMSE of proposed algorithm compared to the three stage method.

This research studies the suitable process of change detection at at an Agricultural areas by focusing on object based method and color fusion. In order to obtain this goal, it is benefit from Landsat7 images. The main idea of offering object based method is a modern algorithm i.e. Double-layer image are combined and An image of the entire layer is formed. Then by selecting suitable parameters a single image is separated in to several parts and by color fusion and object based classification method the changed and unchanged parts are classified. In fact, color fusion is determined by creating different color areas with elementary images that determines changed parts on visual basics and then by using object based classification method and selecting some parts by the user, the total parts of image is determined. Finally, by selecting training samples only one part of image is labeled and its classification is determined and the ultimate map of changes is obtained. Results show that this method is suitable for reducing training samples, increasing exactness (3%-2.5%), speed and increasing information for classification of spatial information and structure and in addition to spectral information it is better than ordinary methods of change detection from comparing 2 multi-temporal images.

In applications related to environmental monitoring and disaster management, multichannel synthetic aperture radar (SAR) data present a great potential, owing both to their insensitivity to atmospheric and Sun-illumination conditions and to the improved discrimination capability they may provide as compared with single-channel SAR. However, exploiting this potential requires accurate and automatic techniques to generate change maps from images acquired over the same geographic region in different polarizations or at different frequencies at different times. Furthermore, sensitivity to contextual information of each pixel reduces the error rates in labeling process, thus generates accurate change maps. The smoothing effect of despeckling and the isotropic formulation of the Markov Random Field model cause over-smoothing of the spatial boundaries between changed and unchanged areas in the final change maps. In order to reduce this drawback, edge-preserving MRF models could be integrated in the labeling process. This method improves the precision of edges at spatial boundaries and increases the change detection accuracy. In this paper, a contextual unsupervised change-detection technique (based on a data-fusion approach) is proposed for two-date multichannel SAR images. A Markov Random Field model is formulated by using “energy functions” that combines the information conveyed by each SAR channel, the spatial contextual information concerning the correlation among neighboring pixels and the edge information. In order to estimate the model parameters, the expectation–maximization algorithm is combined with the recently proposed “method of log-cumulants.” The proposed technique was experimentally validated with semisimulated data produced by ASAR-ENVISAT images. Experiments illustrate a significant improvement (average 12%) with the proposed technique over the other change detection approaches. Integrating edge information yielded accurate results in exploiting various levels of changes (low-medium-high) whereas contextual information and information conveyed by channels were unable to detect low and medium level changes. Considering the small number of iterations, computation time is reduced considerably. Generally the highest accuracy achieved by the proposed algorithm is 99/67%.

Forest canopy density classification map is one of the main sources of information used in forest management. In conventional methods multispectral images are used to generate the map. In this study, aerial panchromatic images as a valuable data source are used to generate this map. Statistical image texture quantization methods including first statistical and second statistical based on GLCM matrix and also geostatistical method used to generate new features from high spatial resolution image. Generated features beside main image used as classification input feature space. Supervised classification was used and about 90% accuracy was obtained. This method is mainly usable in areas with low variety in forest cover type like Zagros and Iran-Turanian region.

Mountain Glaciers are pertinent indicators of climate change and their surface velocity changes, are an essential climate variable. In order to retrieve the climatic signature from surface velocity, large scale study of glacier changes is required. Satellite remote sensing is an effective way to derive mountain glacier surface velocities. In this research, we have conducted a comprehensive assessment of Alam-Chal glacier surface changes (include displacement and velocity), all based on remotely-sensed data. All datasets include aerial photos and satellite images were ortho rectified, normalized and co-registered. By using an aerial photograph collected in 1955 as a baseline and comparing it against a 2003 image collected by the SPOT satellite, the glacier retreat, in direct response to changes in local climate conditions were extracted. Furthermore, we have assessed short-term changes over two-time scales (1988-2003, 2003-2005),using an aerial photo acquired in 1988, a 2003 SPOT image, and a high-resolution Quick Bird image collected over the study area in 2005. We have derived accurate glacier surface velocity vectors (RMSE~2m), based on an FFT-based image cross-correlation technique. Our results point to the capability of the proposed method in accurately retrieving glacier surface changes at a high level of spatial detail, which is important for studies of regional climate change.

To use the information of multi-source data, numerical or analytical fusion techniques are used in combination. The fusion makes for better interpretation and reduce the uncertainty and improve the classification. Several methods have been developed for fusion, including methods based on IHS, wavelet and PCA. In this study, the capabilities of fusion methods based on the IHS and PCA have been examined. In this regard, the methods FFT-PCA, Wavelet-PCA, FFT-IHS and Wavelet-IHS are used in order to fusion on Hyperion L1R data with spatial resolution of 30 meters and panchromatic image Cartosat-1 of IRS satellite with spatial resolution of 2.5 meters. To evaluate the results of the methods some criteria such as correlation coefficient, filtered correlation coefficient, error of least squares, ERGAS and SAM index were used. results of least square error criteria, respectively for, FFT-PCA, Wavelet-PCA, FFT-HIS and Wavelet-IHS for band 15 is 0/020, 0/022, 0/025 and 0/024 and for band 25 is 0/032, 0/035, 0/038 and 0/036 and for band 34 is 0/040, 0/043, 0/044 and 0/041.The results of this criteria and other criterias showed high accuracy of PCA-based methods to fusion of images. The PCA-based methods has advantages over methods based on IHS, such as no band limitation. Of all these methods, the FFT-PCA method provides more accurate results and preserve the spectral and spatial information better than the other methods.

The geometric correction of high resolution satellite images (HRSIs) using proper mathematical models is an important step in 3D spatial information extraction process. To do it, some control information such as points, lines or areas should be extracted at first. Then, the correspondence features are determined and used directly to estimate the unknown parameters of a mathematical model. Since, the accuracy of geometric correction of satellite images directly influence the accuracy of georeferencing procedure, in this paper, the issue of solving the unknown parameters of a rational function model (RFM) based on linear features as well as their combination with point features as control information is studied. In addition, the impact of each control information to reduce or omit the systematic errors are also studied. The obtained results show that the accuracy of rational models based on control lines is very low and about 3 pixels. Additionally, the residual vector diagrams demonstrate the existence of enormous amounts of systematic errors. The reason in addition to the accuracy and distribution of control lines could be due to the characteristics of linear features used as control lines. In other hand, the integration of control lines and points to estimate the unknown parameters of the model increases the accuracy as well as reduces the systematic errors significantly. Therefore, the results show the high potential of the integration of point and linear features to improve the accuracy as well as reducing the systematic errors.

The sub-pixel level mineral information with reasonable accuracy could be a valuable guide to geological and exploration community for expensive ground and/or lab experiments to discover economic deposits. Thus, several studies demonstrate the feasibility of hyperspectral images for sub-pixel mineral mapping.
Target detection is one of the most important applications in hyperspectral remote sensing image analysis. Relative to multispectral sensing, hyperspectral sensing can increase the detectability of pixel and subpixel size targets by exploiting finer detail in the spectral signatures of targets and natural backgrounds. Over the past several years, different algorithms for the detection subpixel targets with known spectral signature have been developed.
Using derivative spectra in analytical chemistry is an established technique which is renowned as derivative spectroscopy. On the other hand, hyperspectral data nature allows the implementation of derivative spectroscopy, in hyperspectral images. Application of this technique in hyperspectral remote sensing is due to its ability for resolving complex spectra of several target species within individual pixels. Several researches have shown that the application of derivative spectrum in target detection, prepare better detection results in each individual pixel.
However, one of the points that we have to pay special attention is that spectrum differentiation eliminates low frequency components of the spectrum. Hence, having a spectra and its best derivative order in a unified approach cause an increase in the information content of a spectral curve. Thus, an ensemble approach was proposed in this research.
To justify the above-mentioned framework, this study was carried out with an airborne hyperspectral data in comparison to the previous works whereby the standard laboratory images were applied. Both proposed detection algorithms were used for identification of four mineral targets including alunite, kaolinite, epidote and hematite which were located in a hydrothermally altered mineral region (Gonabad county) in Iran east (Lat. AWT IMAGE and Lon. AWT IMAGE ). The Hymap images were acquired on September 11th 2006 with a spatial resolution of AWT IMAGE.
Gonabad is structurally a part of Iran’s central desert (Lūt Desert). The regional rock units show the presence of altered mineralized rocks consisting of volcanic and subvolcanic tracit, agglomera, tuff, riolite, riodacite, dacite, etc. These mass rocks were altered due to the effect of hydrothermal solutions. The importance of geological researches in this region comes from above-mentioned hydrothermally altered zones.
Ground sample data were collected for four dominant targets in the study area, including alunite, kaolinite, hematite and epidote. Stratified random sampling strategy was used for collecting the minerals in the field based on geological experts’ opinion. The precise target’s coordinates were determined by using GPS sensors.
After the completion of field sampling procedure, samples were transformed to laboratory. Samples were grained in lab for spectral measurements. Then, reflectance measurements were done using a SVC HR 1024 lab spectrometer covering a full spectral range of VIS/SWIR (350 to 2500 nm). After spectral measurements were recorded, all obtained spectra were resampled to Hymap spectral response function. Finally, the resampled spectra was applied in the proposed ensemble approach for subpixel mapping of above-mentioned mineral targets.
Experimental results show that the proposed method had clearly better detection results in entire understudy samples. The best performance upgrade was about 28, 24, 26 and 16 percent respectively for Alunite, Kaolinite, Hematite and Epidote targets.

Iran as one of the countrieslocated in arid and semi-arid regions of the world, has been in drought danger. Shortage information about long-term weather conditions in many regions of the country, is one of the most important problems in drought monitoring. In this article, spectral vegetation indices (SVIs) have been employed in order to drought modeling and its forecast. To this end, SPI drought indicator (standardized precipitation index) used to represent period of drought and its intensity. Some broad band spectral vegetation indices including Normalized Difference Vegetation Index (NDVI), Temperature Condition Index (TCI) and Vegetation Condition Index (VCI) were extracted by using NOAA-AVHRR satellite imagery. These indices entered to SVM classifier model to gain the SPI index as its result. After comparing the results, TCI was diagnosed as the best index to predict drought condition via 3 months SPI (trimester SPI).