فهرست مطالب f. tabib mahmoudi

Considering the drought and global warming, it is very important to monitor changes in water bodies for surface water management and preserve water resources in the natural ecosystem. For this purpose, using the appropriate spectral indices has high capabilities to distinguish surface water bodies from other land covers. This research has a special consideration to the effect of different types of land covers around water bodies. For this reason, two different water bodies, lake and wetland, have been used to evaluate the implementation results. The main objective of this research is to evaluate the capabilities of the genetic algorithm in optimum selection of the spectral indices extracted from Sentinel-2 satellite image due to distinguish surface water bodies in two case studies: 1) the pure water behind the Karkheh dam and 2) the Shadegan wetland having water mixed with vegetation. In this regard, the set of optimal indices is obtained with the genetic algorithm followed by the support vector machine (SVM) classifier. The evaluation of the classification results based on the optimum selected spectral indices showed that the overall accuracy and Kappa coefficient of the recognized surface water bodies are 98.18 and 0.9827 in the Karkheh dam and 98.04 and 0.93 in Shadegan wetland, respectively. Evaluation of each of the spectral indices measured in both study areas was carried out using quantitative decision tree (DT) classifier. The best obtained DT classification results show the improvements in overall accuracy by 1.42% in the Karkheh Dam area and 1.56% in the Shadegan Wetland area based on the optimum selected indices by genetic algorithm followed by SVM classifier. Moreover, the obtained classification results are superior compared with Random Forest classifier using the optimized set of spectral features. Applying the genetic algorithm on the spectral indices was able to obtain two optimal sets of effective indices that have the highest amount of accuracy in classifying water bodies from other land cover objects in the study areas. Considering the collective performance, genetic algorithm selects an optimal set of indices that can detect water bodies more accurately than any single index.

The increasing population of the large cities has led to developing new constructions in areas around cities to create settlements for the overflow of the population.Such changes in the natural land cover not only disturb the heat balance, but also have negative effects on the landscape, energy efficiency, health and quality of human life. Therefore, it is important for urban planners and managers to be aware of the changes in land cover and land use, especially in metropolitan areas, during long-term periods of time, in order to evaluate and predict the problems caused by these changes. Multi-temporal remote sensing data are one the powerful tools fordetecting land use/cover changes due to the increasing urban growth and then, for updating the three dimensional city models.

In this paper, the impact of Mehr Pardis housing construction is investigated on the land use/cover changes. The proposed land use/ cover change detection strategy in this paper is a post-classification method based on performing object based image analysis procedure. For this reason, Landsat satellite images have been used in 17 years’ time interval, between 2002 and 2019. After performing initial image processing and image segmentation, the three object classes of residential buildings, vegetation, and soil were identified by the object based image analysis procedure. Then, post-classification change detection performed on the generated object based classification maps of both 2002 and 2019 epochs. For change detection in this research, while comparing and contrasting the classes of recognized objects in the classification maps, the results of revealing the changes in the environment, including determining the amount of increase in constructions, changes in the area of soil and vegetation It is obtained.

The produced change map and statistical analysis of the post-classification change detection results reveals that the soil object class is decreased for about 17% and built up areas are increased for about 184% in the 17 years’ time interval. Agricultural fields in this study area are mostly destructed due to the developments in constructing built up areas. The increasing amount of about 104% in vegetation covers relates to the trees and grasslands in new constructed built up areas. To evaluate the obtained results of changes detection in this research, the evaluation of classification maps was used. In this regard, the values of the overall accuracy and Kappa coefficient of the land cover/use classification map in 2002 were 98.41% and 0.86, respectively, and for 2019, 97.01% and 0.87, respectively. Using the capabilities of the object-based analysis method in this research, along with the 15-meter spatial accuracy of the Landsat images, made the classification maps have an acceptable accuracy.

Due to the fact that construction is associated with changing the ecosystem, the construction of housing units in Mehr Pardis has led to the destruction of the mountain environment in some areas and the loss of vegetation in other areas. It is illustrated in the produced land use/ cover change map between 2002 and 2019 that the constructions are rapidly increased in Pardis area and this causes the serious impacts on the environment.

Vehicle detection and counting is an important issue for many applications such as remote monitoring, vehicle tracking for security purposes, traffic management, rescue tasks, parking capacity analysis and metropolitan planning. Vehicle monitoring is also an important part of traffic information, crash control, vehicle flow statistics, road network planning and parking position estimation. Remote sensing images are widely used to monitor vehicles, due to the ability of sensors in providing a complete coverage of the area of interest. Compared with satellite imagery, aerial imagery is usually more considered for vehicle detection and traffic monitoring purposes due to the higher spatial resolution. However, this is extremely challenging due to the small size of vehicles, their different types and orientations, and the visual similarity to some other objects, such as air conditioning in buildings, trash cans and road signs in high resolution images. Lots of researches have been carried out on vehicle recognition in aerial images over the past years. These works can be categorized into the two main groups; shallow learning based methods and deep learning based methods. Most of the researches proposed in the deep learning category use Convolution Neural Network (CNN) for automatic features extraction. Although local convolution neural networks have performed well in object recognition from images, their performance in aerial imagery is limited due to the small sizes and orientation of vehicles, the complex background in urban areas, and difficulties in rapid detection due to large covering area. The general strategy that is applied in shallow learning based methods relies on hand crafted features extraction followed by a classifier or cascade of classifiers.

In this paper, a shallow learning based vehicle recognition algorithm is proposed for aerial imagery. This method uses the advantages of object based image analysis and the image pyramid. The proposed automatic vehicle recognition algorithm is a decision fusion strategy between the initial vehicle candidates and land use/cover classification map to modify vehicle recognition results. The initial vehicle candidates are recognized by structural object classification based on image pyramid. The proposed algorithm for initial vehicle candidates generation is composed of four main steps; 1) generating image pyramid, 2) performing image segmentation on the pyramid layer, 3) structural features measurement on the segmented image objects of pyramid layer and 4) performing knowledge based classification of the image segments into the vehicle and no-vehicle classes to produce a binary map containing only the initial candidates of vehicles. The land use/cover classification map is also generated in an object based image analysis procedure. In the final step of the proposed automatic vehicle recognition in this paper, a decision fusion algorithm is performed between initial vehicle candidates and the generated land use/cover classification map. In this procedure, the recognized initial vehicle candidates from pyramid layer should be transferred to the original image resolution by performing inverse pyramid transformation. Then, considering the meaningful neighboring relationships between vehicles and other defined object classes, the final and modified vehicle regions are recognized.

The ability of the proposed vehicle recognition method in this study is evaluated based on Ultracam aerial imagery with spatial resolution of 11 cm and four spectral bands in visible and NIR that is taken from an urban area in southwestern Russia. The extent of this study area is about 5900 to 9100 pixels. The obtained results showed the vehicle recognition accuracy for about 80%. Moreover, %78.87 and 0.71 are respectively the values for overall accuracy and Kappa coefficient of the final classification map from the proposed decision fusion algorithm. The decision fusion algorithm can decrease false positive pixels in the vehicle recognition results by performing reasoning rules based on the relationships between vehicles and other objects such as buildings and roads.

Pan-sharpening algorithms integrate the spectral capabilities of the multispectral imagery with the spatial details of the panchromatic one to obtain a product with confident spectral and spatial resolutions. Due to the large diversities in the utilized pan-sharpening algorithms, occurring spatial and spectral deviations in their results should be recognized by performing the quantitative assessment analysis.

In this research, the pan-sharpened images from PCA, IHS, and Gram-Schmidt transformation based algorithms are evaluated for the multi-spectral and panchromatic images fusion of Landsat-8 OLI sensor (medium scale resolution satellite) and WorldView-2 (high-resolution satellite). Quantitative analysis is performed on the pan-sharpened products based on the Per-Pixel Deviation (PPD) measure for spectral deviation analysis and high-pass filter and edge extraction measures for analyzing the spatial correlations. Moreover, entropy and standard deviation quantitative evaluation measures are also utilized based on the pan-sharpened image content.

Quantitative analysis represents that increasing the spatial resolution of the utilized remote sensing data has direct impacts on the spectral, spatial, and content-based characteristics of the generated Pan-sharpened products. Gram-Schmidt transformation based pan-sharpening method has the least spectral deviations in both WorldView-2 and Landsat-8 satellite images. But, the amount of spectral, spatial and content-based quantitative measures of PCA and IHS are changing with various spatial resolutions.

it can be said that Gram-Schmidt pan-sharpening method has the best performance in both medium-scale and high-resolution data sets based on the spectral, spatial, and content quantitative evaluation results. The IHS pan-sharpening method has better performance than the PCA method in Landsat-8 OLI data. But, by increasing the spatial resolution of the data, PCA generates pan-sharpened products with better spectral, spatial, and content based quantitative evaluation results.The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

Information about vegetation cover and their health has always been interesting to ecologists due to its importance in terms of habitat, energy production and other important characteristics of plants on the earth planet. Nowadays, developments in remote sensing technologies caused more remotely sensed data accessible to researchers. The combination of these data improves the object classification and recognition results. Recently, hyperspectral and Lidar data has been used for vegetation covers classification. The spectral information derived from hyperspectral data is used to classify and identify the vegetation cover. However; due to the spectral similarities between various vegetation types, false positive results are increased. Using relief information extracted from Lidar data can solve these kinds of errors and can be very efficient for improving the object recognition results. Spectral similarities and spatial adjacencies between various kinds of objects, shadow and occluded areas behind high rise objects as well as the complex relationships between various object types lead to the difficulties and ambiguities in vegetation recognition among other objects in urban areas. Therefore, new procedures and higher levels of modifications should be considered for improving the object recognition results. In recent years, the multi-agent systems have been considered as one of the most powerful tools for solving the problems of automatic object recognition in urban areas.

According to the difficulties of vegetation recognition in complex urban areas, the proposed object recognition in this paper is a decision level fusion strategy between hyperspectral and Lidar data based on the capabilities of the multi-agent systems. Vegetation indices from hyperspectral image are used as spectral features in the knowledge base. Moreover, digital surface model which is produced from Lidar data is used for height features extraction. After producing a rich knowledge base containing the spectral and height based features, the proposed hierarchical classification is performed which is composed of two steps; step 1: initial vegetation candidate recognition, step 2: vegetation classification based on the capabilities of the multi-agent systems. Applying the optimum thresholds on the normalized difference vegetation index in the first step produces a binary image containing the initial vegetation candidates. The multi-agent system in the second step of the proposed method in this paper contains several object recognition agents (one agent per each vegetation cover type), a coordinator agent and a yellow page. The object recognition agents have three layered internal architecture and use the belief-desire-intention (BDI) reasoning model. 

The capabilities of the proposed multi-agent vegetation recognition algorithm in this paper is evaluated based on the hyperspectral and Lidar data collected from the University of Houston and the surrounding areas. Four object recognition agents are defined for trees, healthy grass, water-stress grass and artificial grass. These four object recognition agents perform their reasoning based on the pre-defined spectral and height features in the knowledge base. The obtained results indicate the overall accuracy of about 87% from the proposed multi-agent hyperspectral and Lidar decision fusion strategy. The obtained results from performing the same multi-agent system only on the hyperspectral image (without considering Lidar data) have the overall accuracy for about 71%.