Forest biomass and estimate its value has a significant role in climate change. Because of land constraints and time-consuming methods to estimate biomass, using remote sensing is an effective alternative to terrestrial methods. In this study, in order to improve the accuracy of estimates of forest biomass to earlier research, optical image AVNIR-2 and PALSAR radar satellite ALOS images used with data from ground-based College of Agriculture, Tehran University of North region Kheiroudkenar. This stude procedure respectively 1 - features extraction from images, 2 - select features using genetic algorithms, 3 - Biomass estimated with features selected by regression analysis and neural networks. Evaluating the results of the application of neural networks and regression analysis on the features selected by genetic algorithms, neural networks represent the accuracy over 70 percent and regression analysis represent the accuracy to about 15 percent. For this reason, the use of neural networks in a way that has been used in this study for the northern forests and the complex structures is recommended.

Synthetic aperture radar (SAR) images due to the usage of coherent imaging systems are affected by speckle. Thus, lots of despeckling filters have been introduced up to now to suppress the speckle. Hence, objective and subjective evaluations of the denoised SAR images become necessity. Many objective evaluating estimators have been introduced to evaluate the performance of despeckling filters. However, two main problems exist when evaluating the SAR images: 1) contradiction of objective and subjective evaluations and 2) absence of the ground-truth (noiseless) SAR image of the illuminated scene. Lots of efforts had been made to introduce precise referenceless estimators for SAR images which will be compatible with subjective evaluation and the results obtained by other estimators. In this paper, we propose a new edge detector and also a new referenceless estimator called “ Extended Ratio Edge Detector” and “ E-α β ” , respectively. These algorithms are the extended version of “ Ratio Edge Detector” and “ α β ” estimator. Experiments on images obtained from RADARSAT-1 dataset showed that the proposed edge detector and the estimator outperform their previous versions of algorithms as the proposed E-α β parameter subjectively reports up to 0. 2 better results for images filtered with FANS filter in comparison with other used methods. This is also validated by β ratio and μ ratio parameters. Therefore, it is a reliable tool for objective evaluation of despeckled SAR images.

One of the destructive factors in denotation scene details from the Synthetic Aperture radar images is the presence of speckle noise in it. Different method of image denoising have been proposed using the Sparse Representation (SR) Technique, which, eliminates the noise of the image properly by preserving details. Because of the Computational Complexity of these methods and the large dimensions of SAR images, their use in SAR images is challenging. This paper presents a new method in SAR image denoising by SR that reduce run time with preservation of image quality. In this method, the denoising performs in two step. Firstly, the image is filtered using a simple denoising method, and the removed details is then retrieved using the SR technique and added to the filtered image. By retrieving details from the heterogeneous regions of the image and using random sampling matrix in reconstruction image, the processing volume and the required memory in using SR technique are reduced. Simulation results show that this method, with preservation of image quality, has an operating time of about 0. 2 of other methods.

Synthetic aperture radar (SAR) imaging widely used for remote sensing and military applications; because this kind of imaging is independent from the weather and has high resolution in day and night. In this paper, a new method based on fuzzy logic is proposed for the edge extraction of SAR images. Edge detection is one of the most important techniques used for data analysis and decision-making on the image for various applications. Edge detection is a scope of research in image processing and feature extraction. A serious problem concerning SAR images is that they are intrinsically disturbed by speckle noise. The existence of speckle noise, intensely impedes the interpretation and analysis of SAR images. So, traditional techniques can not provide good edge detection results for SAR images. The method, proposed in this manuscript, by optimization of the fuzzy inference system (FIS) i.e using proper kernels and membership functions, the edge extraction of SAR image is done. The inputs for FIS are image gradients (in both horizontal and vertical directions) and a heuristic self-tuning edge detector parameter which is shown by. This self-tuning parameter is variable for different images and by controlling the shape of Gaussian membership function leads to automatization of proposed algorithm. Finally, the proposed algorithm is compared with other edge detectors to demonstrate the superiority of the proposed method. In this work, in addition to structural similarity criterion, the Mean squared error (MSE) and Peak signal-to-noise ratio (PSNR) are used to numerical Analysis.

Automatic extraction of road from satellite images is one of the most important researches in the field of remote sensing. The method proposed in this study is based on a fuzzy method for detection of road areas from high resolution SAR images. In this method, the multiple features are extracted first based on the backscatter coefficient of each pixel and its neighbor pixels from the input image. The extracted features are combined with each other in the next step using a fuzzy algorithm and the desired road areas are selected separately in the last step considering the spatial and spectral criteria. The favorite results and root mean square of 78% were obtained by applying this algorithm on high resolution SAR images obtained from the TerraSAR satellite.

In the past years, a number of algorithms have been introduced for synthesis aperture radar (SAR) imaging. However, they all suffer from the same problem: The data size to process is considerably large. In recent years, compressive sensing and sparse representation of the signal in SAR have gained a significant research interest. This method offers the advantage of reducing the sampling rate but also suffers from speed processing limitation and it needs a huge amount of memory to reconstruct the image. On the other hand, inaccuracy in SAR model induces phase error to the results and makes the reconstructed image blurry. Existing sparse methods in the presence of phase error, have high computational costs and need a lot of processing time. In addition, these methods take up considerable space in the memory for saving the measurement matrix. In this paper, a fast method is proposed to reduce the computational cost of image reconstruction, based on the signal sparsity in the presence of phase error. The proposed method consists of substituting accurate observations of sparsity methods with approximated observations of matched filter methods. In this method, the output of Range-Doppler matched filter is reconstructed with sparse representation, and error phase is estimated simultaneously. This method leads to a nonconvex optimization problem and to solve that, we use the majorization minimization method. The phase error and reconstructed image are estimated in an iterative procedure. The use of approximated observation, eliminates the need for carrying out big matrix multiplications, and Fast Fourier Transformation, as a low computational cost operation, can be employed instead. In addition to computation speed, this method does not need any memory space for saving measurement matrices. In our numerical simulations, we compared the speed of processing and the mean square error (MSE) of reconstructed images for the proposed method with the state-of-the-art sparse method for different sizes of image and under-sampling rates. It is shown in simulations that the reconstructed image from our method has a slightly lower quality and higher MSE, because of the sidelobes effect of the matched filter output. However, in certain conditions, the speed of the proposed method is more than a hundred times faster than the compared method. The achieved processing speed with no need for the memory to store the measurement matrix at the expense of slightly lower image quality would be acceptable for most applications.

Different approaches have already been proposed for the analysis of the scattering field of targets. However, in practice, there are numerous targets for which, the calculation of the dispersion analysis is highly complex. To solve this problem, point target model is taken into account. This model aims to replace the target with a finite number of points that are dominant in their vicinity in terms of the radar cross section. These points, which are called scattering center or phase center, are local maximum in terms of the radar cross section and are effective in many practical applications such as radar imaging of targets, analysis of the dispersion, extraction of the target scattering mechanisms, Stealth technology, remote sensing, passive defence, etc. The present study is an attempt to extract the point target model for the complex, distributed targets using SAR image. For this purpose, image production and extraction of the dominating points have been conducted in two stages. All the simulations have been performed in MATLAB and CST software environments.

Change detection using remotely sensed data has been used in many applications, such as the detection of dynamic changes in land cover and land use, the monitoring of forestland and agricultural land, the assessment of damage from natural disasters, and the study of urban environments. Despite the numerous studies devoted to multispectral and hyperspectral imagery, however, the use of optical imaging sensors is limited to weather conditions. Synthetic aperture radar (SAR) sensors can obtain daylight, cloud coverage, and weather-independent images. Their backscattered signals are also sensitive to the form, orientation, homogeneity, and surface conditions of a target. SAR imagery can therefore serve as a useful tool for detecting land cover and land use changes. The development of SAR techniques has given rise to Polarimetric SAR (PolSAR) systems, which measure four linear polarization channels (i.e., HH, HV, VH, VV) and the phase differences among them. SAR polarimetry with the functionality of identifying different scattering mechanisms can provide more significant information than single-channel imagery. From the perspective of image analysis unit, Change detection techniques are classified into two categories, namely, pixel-and object-based approaches. Pixel-based methods rely only on the information derived from individual pixels and do not consider the spatial relationships among these pixels. Whereas, the values of neighboring pixels in an image are strongly correlated, and the probability of change occurrence in adjacent regions is more than distinct points. The use of spatial features also effectively reduces the speckle effect in PolSAR images. Given these considerations, object-based approach has been widely used for PolSAR image analysis over recent years. This paper proposes a novel image segmentation algorithm for improving the accuracy of land cover change detection. This method consists of the following three steps: 1) segmenting two PolSAR images by new segmentation technique, namely region based improved watershed; 2) selecting the optimal differential of polarimetric features based on the Genetic Algorithm (GA) and Jefferies-Matusita (JM) distance criteria; and 3) The binary classification of image objects using the differential of mean pixel values of the corresponding image objects. Despite the development of various region-based segmentation methods, watershed segmentation is appropriate for the segmentation of high resolution images based on the many advantages of this morphological algorithm. These advantages include inherent simplicity, high speed implementation, the creation of separated regions in low contrast images, and the provision of closed connected regions. Common watershed segmentation approaches, such as distance transform and the gradient method, cause over-segmentation problem given the noise or local irregularities present in a gradient image. Unlike the direct application of the watershed algorithm, using a marker-controlled approach that involves the incorporation of additional knowledge into a segmentation procedure can limit the number of segmented regions. In this method, the flooding procedure begins from a previously defined set of markers. Markers, as connected components belonging to specific areas of an image, can be defined on the basis of a set of descriptors, such as gray level value, shape, location and texture. Compared with conventional watershed and multi-resolution segmentation methods, the improved watershed reduces the speckle effect in PolSAR images and avoids the over- segmentation problem. The results of proposed change detection method on Uninhabited Aerial Vehicle Synthetic Aperture (UAVSAR) full polarimetric images achieve the overall accuracy of 92.40% and the 0.85 kappa coefficient.

Nowadays, Earth observation (EO) technology became an indispensable tool to help environmental monitoring, as well as their changes, for natural resources management, urban planning and development, water management and land use planning. In particular, radar EOs, unlike the optical ones, can be collected regardless of illumination and weather conditions. Multitemporal polarimetric synthetic aperture radar (PolSAR) images are useful source of information for detection and mapping the environmental changes, especially in wide areas, during the day and night and all weather conditions. Change detection methods can identify the change or no change conditions in land covers using the time series observations. In this paper a method is proposed for change detection in SAR remote sensing images. This method is based on the Change Point Analysis. The cumulative frequency of difference image, which contains the environmental changes, normally follows a specific class of statistical distribution. Gaussian mixture model is one of the most suitable models for Change Point Analysis. This model can efficiently estimate the parameters of mixture distribution. The intersection point of two distributions is a change point, which can be seen as a threshold. This threshold is then used to separate the change and no change classes. The proposed method is implemented and analyzed using three SAR data sets. The analytical evaluations of the final change maps from two of these data sets with reference data had the Kappa coefficients of 90% and 96% respectively. The other data set contained the multitemporal PolSAR images and had been acquired over an agricultural area. The changes in these images were enough reliable to be connected to the agricultural activities, such as crop growing stages and harvesting, based on an available crop map. Finally, the method was evaluated against the Otsu method, as one of the best threshold estimation methods, and the results showed the superiority of the proposed method, e.g.2% better in term of kappa coefficient.. As a result, the proposed method, can be efficiently employed for land cover change detection and monition in natural resources management.

Therefore, in this article our studies were focused on this problem. In order to correct these errors, an independent source of information was required such as imaging from another angle, topographic map or DEM. In this paper, a method for geometric calibration of SAR images is proposed. The method uses Range-Doppler (RD) equations and to implement the method used in this article, two SAR datasets are tested with RD modelling. These datasets are acquired by ALOS PALSAR spaceborne SAR sensor. Test areas covered by these datasets range from flat plains to mountainous areas, which the first dataset located in the border between United States and Mexico and the second one is in Iran. In this method, for the image georeferencing, the appropriate Digital Elevation Model (DEM) and also exact ephemeris data of the sensor is required. In the algorithm proposed in this paper, first digital elevation model transmit to range and azimuth direction. By applying this process, errors caused by topography such as foreshortening is removed in the transferred DEM. Then, the original image is registered to transfer DEM by transformation equations. The output is a georeferenced image without geometric distortions. The advantage of the method described in this article is in eliminating the requirement for any control point as well as the need for attitude and rotational parameters of the sensor. Furthermore, two experiments with different settings are designed and conducted to comprehensively evaluate the accuracy of the SAR georeferencing with RD model. Few experiments are done in this study for various purposes. The first one is to find the best transformation equation among the three types for registering images. In the first experiment the efficacy of three types of transformation equations on georeferencing of ALOS PALSAR images were evaluated with identified check points. To evaluate the accuracy of the georeferenced images, 25 check points in different parts of the image was selected. By comparing the obtained coordinates in georeferenced image and reference points in Google Earth, the RMSE was calculated for these points. In best situation, the planimetry accuracy were 20.11m for dataset A and 19.94m for dataset B and the altimetry accuracy were 30.28m for dataset A and 30.71m for dataset B. Since the ground resolution of multi-look image was 30 meters, the planimetry accuracy achieved in this research is acceptable. The other experiment is to compare the georeferenced SAR images generated from three DEMs to demonstrate the effectiveness of DEM spatial resolution on the accuracy of georeferencing SAR images. In addition we investigated the suitability of three typical DEM datasets for SAR georeferencing in RD model. The experimental results show that the best transferred DEM was obtained from the ASTER DEM of spatial resolution comparable to that of ALOS PALSAR images.