Mines with their displacements cause damage to infrastructure and land, so it is important to control the displacement caused by mines. Also, the amount of minerals extracted from mines has always been of interest to managers. On the other hand, radar interferometry, given the production of repetitive and full images and the acceptable accuracy of this method, in examining be suggested for the study of displacements caused by open-pit mines against methods such as Leveling and Global Positioning System that are generally expensive and timeconsuming. The surveyor engineers are responsible for calculating the displacement and volume of excavation in open-pit mines using the Leveling and Global Positioning System. The purpose of this study was to calculate the displacement caused by excavation in open-pit mines and calculating the volume of excavation. Because of the large-scale displacement in open-pit mines, the maximum displacement that can be extracted by radar interferometry technology is controlled by two factors: a) the wavelength of the radar images and b) the dimensions of the radar images, by increasing the wavelength of the images or reducing the pixel dimensions of the radar images, the maximum displacement gradient generated by the interferometric radar can be achieved.
On the other hand, rapid exploitation in open-pit mines creates decorrelation between the SAR images; To eliminate decorrelation images, selecting images with small spatial and time baselines can solve this problem. In this study, the Gol Gohar mine in Sirjan was studied using Envisat satellite radar images. Radar image processing was also performed using StaMPS software. In this study, the displacement rate is increased by changing the amount of multi looking factor images from 20 for azimuth and 4 for range to 5 for azimuth and 1 for range (reducing image pixel dimensions). After reducing the pixel dimensions of the image the maximum displacement obtained for a point in the mine changed from -51 mm to -75 mm, and also extracted volume changed from 61,000 cubic meters to 67,000 cubic meters after. So, by reducing the pixel dimensions of the radar images, the amount of displacement gradient is more detectable, and the value volume of excavation from mine will be more realistic.

Synthetic Aperture Radar (SAR) images are inherently affected by a multiplicative noise-like phenomenon called speckle, which is indeed the nature of all coherent systems. Speckle decreases the performance of almost all the information extraction methods such as classification, segmentation, and change detection, therefore speckle must be suppressed. Despeckling can be applied by the multilooking method when the image is formed or by spatial filters after the image formation. However, multilooking decreases the spatial resolution. Moreover, the performance of spatial filters depends on the size and the orientation of used window’s kernel. To overcome these limitations, Multi-Resolution Analysis (MRA), e.g., Wavelet Transform (WT), can be used. In this article, based on the intra-scale and inter-scale dependencies of wavelet coefficients and by employing the Maximum a Posteriori (MAP) estimator, a method for denoising the wavelet coefficients was proposed. Distributions of noise and noise-free wavelet coefficients in wavelet domain were considered as bivariate Gaussian and bivariate circular symmetric Laplace PDFs, respectively. For comparison analysis, Lee and Frost filters were used, also several classical thresholding methods such as VisuShrink, SureShrink, and BayesShrink were employed. Peak Signal-to-Noise Ratio (PSNR) and edge-preserving index beta were used to evaluate the simulated SAR data. Also, Equivalent Number of Looks (ENL) was employed for real SAR data. Experimental results showed that the proposed despeckling method performed more efficiently to suppress the speckle and preserve the edges than others. For instance, the PSNR and beta values that computed for 16 looks simulated SAR data were equal to 30.42 and 0.734, respectively. Also, the ENL values for region 1 of Noerdlinger and San Francisco images corresponded to 71.12 and 34.57, respectively.

New generation of space radar sensors with high spatial resolution, heralds the possibility of these images usage for automatic feature extraction, specially road ones related. This study compares road extraction in high resolution radar and optical images. IKONOS and TerraSAR-X images are used for this purpose. In order to make possible the comparison, the images have been coregistered. Then, texture features have been extracted and classified using a back propagation neural network. By comparing the results with reference road network, provided by the operator, for TerraSAR-X and IKONOS images respectively, 46.10 and 57.72 percent for RCC, 46.58 and 93.27 percent for BCC and 0.61 and 0.31 for RMSE were obtained. Comparison of the outputs of two algorithms showed that each of optics and radar images have some defects in road extraction. For example, optical algorithms are sensitive to image parts which have the same texture and spectral features similar to roads such as parkings and roofs. In one hand, these areas have visible appearance and different texture in radar images, so radar images in urban areas especially with narrow roads and alleys, are suitable. On the other hand in areas with dense vegetation, Radar images do not perform well, while the optical images are able to distinguish the mentioned areas. In order to eliminate defects and increase the accuracy of the present algorithm, because of high functionality of these images, fusion of them seems to be an efficient wayin developing algorithms.

Due to capabilities of imaging radar, there has been an enormous surge of interest in microwave imaging technology. Unlike optical imaging, understanding the theoretical underpinnings of imaging radar can be challenging, particularly when new to the field. The technology is relatively complicated, and understanding the interaction of the incident microwave energy with the landscape to form an image has a degree of complexity well beyond that normally encountered in optical imaging. The aim of this paper is to assess the use of RADARSAT data for estimation of rice vegetation indices. The radar backscatter coefficient of rice fields appears to have a significant temporal variation. Due to weather conditions in the north of Iran, microwave sensors can be more effective in monitoring rice growth than optical sensors, since a longer wavelength electromagnetic wave is less affected by clouds and precipitation events. The backscattering measurements of rice-growing areas have already been acquired using satellite synthetic aperture radars. Time series RADARSAT fine beam mode data was acquired from May till August 1998 for seashore of Behshahr, Behshahr seashore of Mazandaran province to assess and monitor rice crop from the space. Vegetation indices (VIs) are among the oldest tools in remote sensing studies. Although many variations exist, most of them are coefficients of the reflection of light in the red and NIR ranges of the electromagnetic spectrum to separate the landscape into water, soil, and vegetation. To determine the density of green on a patch of land, researchers must observe the distinct colors (wavelengths) of visible and near-infrared sunlight reflected by the plants. As can be seen through a prism, many different wavelengths make up the spectrum of sunlight. When sunlight strikes objects, certain wavelengths of this spectrum are absorbed and other wavelengths are reflected. The pigment in plant leaves, chlorophyll, strongly absorbs visible light (from 0.4 to 0.7 µm) for use in photosynthesis. The cell structure of the leaves, on the other hand, strongly reflects near-infrared light (from 0.7 to 1.1 µm). The more leaves a plant has, the more these wavelengths of light are affected. Theoretical analyses and field studies have shown that VIs are near-linearly related to photo synthetically active radiation absorbed by a plant canopy, and therefore to light-dependent physiological processes, such as photosynthesis, occurring in the upper canopy. Practical studies have used time-series VIs to measure primary production and evapotranspiration. In this paper 5 VIs was investigated and compared with radar backscatter coefficient of rice and made a mathematical linear regression model with the correlation coefficients for estimation VIs from RADAR images. This research is a descriptive-analytical study based on acquired data and statistical methods. The following stages and procedures are to be considered: Pre-processing stage; included: 1. Co registration 2. Calibration (speckle reduction) 3. and extraction from time series of RADAR images. Atmospheric correction of optical Landsat images with FLAASH (MODTRAN4) module. processing stage; included: 1. Converting DN into radiance and reflectance coefficients in optical bands of Landsat images 2. Generating NDVI, DVI, IPVI, SR, RDVI, vegetation indices from three Landsat images (red and near infrared bands). 3. Extraction of statistical parameters in 10 test site same as RADAR images. Calculation of obtained statistical data with MATLAB software and creation of linear regression equations and correlation coefficients. To further explore the relationship between paddy growth stage and radar backscatter, mean backscatter values were calculated for all the test fields for three different dates. The plants showed very low backscattering in early stage of plantation –12dB to –10dB. It started increasing up to –6 dB during vegetative phase of the plants, which is due to increase in height as well canopy cover. There was an increase up to –5 dB further in reproductive stage of the plants. During ripening phase, backscatter remained almost same until the field was being harvested. This is due to not much change in plant growth during the ripening period. All considered VIs in this research shows increasing in reflectance proportional to paddy growth stage. Because of high correlation between red and near infrared bands in optical images with chlorophylls and fresh biomass of plant (VIs) and again, high correlation between radar backscatter coefficients and biophysical parameters (content of water, canopy, height plant, plant structure), we can make a connection between those statistical parameters and create a mathematical model (simple linear regression equations) with different correlation coefficients. The results showed that the NDVI with R=0.92 has the best performance among the other four VIs. NDVI is calculated from the visible and near-infrared light reflected by vegetation. Healthy vegetation (left) absorbs most of the visible light that hits it, and reflects a large portion of the near-infrared light. Unhealthy or sparse vegetation (right) reflects more visible light and less near-infrared light. The numbers on the figure above are representative of actual values, but real vegetation is much more varied.

In this paper, we propose an unsupervised method for change detection in polarimetric synthetic aperture radar (PolSAR) images. The symmetric revised Wishart (SRW) is applied for measuring the similarity of two multilook complex (MLC) covariance matrices. The SRW produces a scalar feature image as an input into the automatic thresholding algorithm which is aimed to distinguish change from no change. In particular, the Kittler and Illingworth minimum-error thresholding method is generalized to model the non-Gaussian distribution of change and no change classes. Experimental results on bi-temporal simulated and RADARSAT-2 C-band PolSAR data confirm the effectiveness of the proposed method. The results of the real data also demonstrate that the multipolarization SAR improves the detection accuracy and lowers the overall error rate of the method compared to single-polarisation SAR.
Multitemporal satellite remotely sensed data from a geographical area offers a great potential for monitoring and detecting changes in Earth’s surface (for e.g. damage assessment in natural disasters [1], monitoring the changes in agricultural areas [2], and glacier change detection [3]). Synthetic aperture radar (SAR) is an important instrument in remote sensing, providing measurements insensitive to the sun-light and atmospheric conditions. Furthermore, polarimetric synthetic aperture radar (PolSAR) sensors provide data with increased discrimination capability as compared to single-channel SAR and also insensitive to the sun-light and atmospheric conditions.
Several change detection algorithms in SAR data has been developed in the literature, e.g., [4]-[5]. Unsupervised change detection is generally performed in three steps: 1. image preprocessing including co-registration, speckle filtering, and radiometric and geometric terrain corrections, 2. comparing SAR image pairs with a desired test statistic, and 3. finally, a thresholding method is applied to the test statistic to achieve the final change map.
Fig. 1: the block diagram of the proposed change detection approach
In the analysis of change detection in multilook complex (MLC) PolSAR images, the backscattered signal is represented by the so-called polarimetric sample covariance (or coherency) matrix. In [2], the Wishart likelihood ratio test is proposed as a test statistic for change detection in multilook PolSAR images. Akbari et al. utilized the complex kind Hotelling-Lawley trace statistic as a new test statistic for change detection in multilook PolSAR images [18]-[19]. In [20], Ghanbari et al. applied the symmetric revised Wishart (SRW) distance in [8] as a test statistic for detecting the changes between two Wishart distributed multilook covariance matrices.
Changes are finally detected by a decision threshold to the test statistic to distinguish change from no-change. In the present paper, the thresholding is performed using the generalized Kittler and Illingworth’s minimum-error algorithm (K&I for short) [10] on the SRW image. In the proposed method, the generalized Gamma distribution, denoted GΓD, is applied for modeling change and no-change classes in the SRW image. The GΓD was first proposed by Stacy [15] and has been widely applied in many fields, e.g., [14]. This distribution has a highly fixable form and good fitting capability to the histograms of change and no-change classes. Parameters of the probability density function (PDF) in this study are estimated using the method of log-cumulants (MoLC). This estimation method has been adopted in the analysis and processing of SAR images, e.g., [16]-[17]. The block diagram of the proposed unsupervised change detection method is presented in Fig. 1.

The ability to interpret the image is one of the key and important features in the ability of imaging systems, which is usually used in the concept of the ability to extract information from the image. Quantifying this feature is one of the main challenges in the fields of information content and image integration in remote sensing, which is needed and important in various applications such as target detection. By evaluating the interpretability of remote sensing images, it is possible to estimate the appropriate image characteristics for various target detection applications.
In general, the interpretability of remote sensing images depends on the conditions of imaging, environment and target. Polarimetric radar images are a very useful source of data in target detection applications with more resolution than single-channel radar images. So far, various methods and models have been presented to evaluate the performance of detection in polarimetric radar images; most of them are based on the theory of signal detection or based on the probability of target detection and false alarm. National Image Interpretability Rating Scale (NIIRS) models, the automatic target detection (ATD) and simultaneous tracking and tracking and recognition (STAR) models are among the common and conventional methods of evaluating the interpretability of radar images in target detection applications. The NIIRS radar is based on the general image quality equation (GIQE) and takes into account the parameters of resolution, landing angle, squint and background type. The ATD model is designed based on false alarm analysis and estimates the measure of performance (MoP) according to available ground information. The STAR model is also based on the modelling of parameters related to the environment, target and sensor and calculates the probability of success in detecting, detecting and identifying the target in the radar image for a target with specific dimensions and physical conditions.
Proposed

In this study, a unified method has been presented to evaluate the interpretability of polarimetric radar images in the application of target detection. In this method, the performance of the radar detector is first calculated based on the parameters of the detector in the STAR model. After that, for each of the images related to the channels and polarimetric parameters, the target detection performance is calculated by calculating the performance measure (MoP), which relates the detection probability to the false alarm rate is determined. Finally, by combining the information obtained from the MoP criterion and the STAR model, the interpretability of polarimetric radar images is calculated.
Experimental

In this study, the L-band polarimetric radar dataset related to the Flevoland region of the Netherlands, which was obtained by the AIRSAR sensor in 2009, is used to evaluate the effectiveness of the proposed method. Based on the obtained results, the proposed method increases the probability of detecting, detecting and identifying the target by 30% compared to the STAR model.
For the development of this research, it is suggested to improve the performance of the radar sensor by using the techniques of combining radar data with optical data and auxiliary data. This case, especially in tracking targets in complex environments, can significantly improve continuous target tracking.

Launching satellites with Synthetic Aperture Radar (SAR) sensors onboard, purposely built for differential interferometry (D-InSAR) applications opened new opportunities for mapping and monitoring slow ground deformations and even high speed landslides, for the updating of regional landslide inventories as well as retrieving time series of landslide movements. The present work has been done by using DInSAR method, with objection of performance assessment of two radar sensors in order to detecting, monitoring and landslide inventory mapping in Dena Mountain of Zagros located in south of Isfahan province. For this purpose two sets of images including 17 and 21 RADAR images of PALSAR and ASAR sensors from ALOS and ENVISAT satellites were chosen and processed by D-InSAR method. The preliminary processing result showed that 16 interferometer images were suitable for the next processing stages from all applied data sets. The outcome of interfrogram analysis along with field checking showed that 914 segments are marked as landslides anomaly over the area of 13578 ha. The maximum land sliding activities was identified as 1.58 meter vertical displacement during May 2010 to September 2011. Furthermore, results showed differential interferometry method applied for above mentioned frequencies has sufficient accuracy and conform efficiency for landslide detection, monitoring and distribution mapping compared to other methods (field survey in extend area, aerial photo and optical satellite images). Out of this two frequency, low frequency of PALSAR images has better ability in landslide zonation due to its capabilities such as longer wavelength and reduced incoherency. Where the high frequency of ASAR images can offer more details of the landslides due to its shorter wavelength.

Synthetic Aperture Radar (SAR) is the only way to evaluate deformation of the Earth’s surface from space on the order of centimeters and millimeters due to its coherent nature and short wavelengths. Hence, by this means the long term risk monitoring and security are performed as precisely as possible. Traditional SAR imaging delivers a projection of the 3-D object to the two dimensional (2-D) azimuth-range (x-r) plane. Due to the side looking geometry of SAR sensors problems like foreshortening, layover and shadow should be dealt with. For overcoming the problem of layover in high urban environment different SARtomography methods are developed. The aim of SARtomography methods are not only overcoming layover problem but unambiguous 3-D and 4-D (space-time) dynamic map of the city can be achieved. The assumption, made by the classical interferometric techniques (i.e.: PSI), the present of a single scatterer per pixel, neglects the fact of occurrence of mulitiple scatterers. For instance PSI initial assumption is the presence of single sactterer in each azimuth-cell range. However, this assumption is not plausible in a high rise urban environment where suffers from the presence of multiple scatter. Furthermore, by the advent of Very High Resolution sensors like Cosmos-Skymed (1,2,3) constellation SARtomography is revolutionized. Needless to say that due to the very high resolution of the images (up to 1m resolution), precise shape and deformation of each individual building can be obtained. Nonetheless, it has to be noted that the increasingly impact of layover on new generation of VHR sensors. To this end several practical SARtomography methods such as first order model and Non Linear least Square (NLS) are introduced. This paper has presented the capacity of the new class of VHR spaceborne SAR systems, like COSMO-Skymed, for TomoSAR processing in high urban environment. However, particular problems related to the side looking geometry of SAR has proved to be more obvious comparing to the previous generation of high resolution sensors (10 m resolution): Layover is one of them. The main aim of this paper is at comparing SARtomography methods and their advantage and disadvantage to the older version of SAR methods like PSI for monitoring high urban environment. The project has been implemented on Very High Resolution (VHR) Cosmo-skymed Stripmap mode (up to 3m azimuth-range resolution) images from Astana-Kazakhstan. Like PSI, TomoSAR benefits greatly from the high resolution of Cosmo-skymed data, as the density of coherent pixels and the signal-to-clutter ratio increase significantly with resolution. The results reveal that the number of permanent scatterers found by NLS and first order model are far more than PS method which indicate the superiority of these methods in overcoming the layover problem in high environment urban areas comparing to the PSI; Besides, the mean deformation velocity, height and coherence of every scatterer were obtained by SARtomographic methods are compared with PSI. As it comes to tomographic processing methods different factors must be taken into account such as the accuracy in estimating the heights, computational cost and the model order selection. Results indicate that the first order method has low computational cost but it suffers severely from side-lobes on the other hand the computational cost of NLSM is very high but it is an accurate method as long as the correct model order is selected. SARTomography proved to be an efficient method for detecting multiple scattrers and layover removal in high urban environment.

Temporal and geometrical decorrelation often prevents SAR conventional interferometry from being an operational tool for surface deformation monitoring. Persistent Scatterer Interferometry (PSI) techniques presented to overcome the limitation of SAR conventional interferometry and use amplitude analysis and considering temporal deformation model for PS pixel selection fail to identify PS pixels in rural areas lacking man-made structures. On the other hand, the high subsidence rates lead to not be fulfilled the required condition for unwrapping (Nyquist sampling criterion) and significant phase unwrapping errors in novel PSI algorithm (StaMPS) that applies amplitude analysis as well as phase stability in order to select the PS pixels without using a pre-define deformation model. Therefore, in this paper we present an enhanced algorithm based on PSI in order to estimate deformation rate in rural area undergoing high and nearly constant deformation rate using the available SAR images. The proposed approach integrates the merits of all existing PSI algorithms in PS pixel selection and phase unwrapping. PS pixels are selected based on the amplitude information and phase stability and their phase are compensated for APS and nonlinear deformation contribution by applying temporal filter. Deformation rate is then estimated using LAMBDA method that is a fast and optimal without considering Niquist sampling criterion. The approach was applied to the ENVISAT ASAR images of Southwestern Tehran basin and the results were evaluated with leveling data and the maximum difference rate across the leveling stations was 5 cm/yr demonstrating the high performance of the proposed algorithm in comparison with the obtained results from other interferometry methods. Moreover, the presented algorithm was applied to the simulated data and the value of RMSE was obtained 0.003 m/yr confirming this success.