Despeckling And Detection Of SAR Image Via Sparse Representation

As an active sensing, Synthetic Aperture Radar (SAR) plays an important role in situationawareness in sea, and land environments. As one of major sources of ground information, it canprovide powerful support to command and decision. This dissertation addresses issues ofdespeckling and detection in SAR image. As SAR is a coherent imaging system, the informationfor interpretation is carried by the average intensity or Radar Cross Section (RCS) at each speckledpixel. It is obvious that sparse representation has good prospects for the application in SAR imageprocessing. It is important to improve level of research and interpretation by carrying out keytechnology research of the sparse representation in SAR image processing. It is also important forSAR image processing in civil and military applications. It is a complex process. There are someremained theoretical problems to be systematically solved. In this paper, SAR image specklesuppression, target detection and change detection based on the sparse representation theory arediscussed in depth. The main research work and contributions of this paper are shown as follows:Chapter two: A de-speckling algorithm for SAR images using adaptive over-complete learneddictionary is proposed. A K-OLS algorithm for designing overcomplete dictionary for sparserepresentation is proposed. The vector quantization based on K means algorithm andorthogonal least square algorithm (OLS) are used and a practical optimization strategy basedon an iterative loop is used to design a redundant dictionary. A de-speckling algorithm forSAR images using K-OLS algorithm is proposed. Firstly, SAR image is projected into a highdimensional space using the learned dictionary and a sparse representation of SAR image isobtained. Secondly, model for multi-objective optimization problem is built by regulationmethod. Finally, the de-noising process is realized through solution of the multi-objectiveoptimization problem in which the mean backscatter power is reconstructed. The experimentalresults demonstrate that the proposed algorithm has good de-speckling capability.Chapter three: A new methodology for despeckling of SAR images using sparse optimizationmodel is proposed. The algorithm based on sparse representation via over-complete dictionaryhave a strong data sparseness and provide solid modeling assumptions for data sets. Firstly, asparse optimization model based on structural properties of SAR image is built by regulation.Secondly, a practical optimization strategy is used to design a redundancy dictionary. And then,a over-complete dictionary is constructed by employing a combined dictionary consisting of wavelets, shearlets and redundancy dictionary. Finally, the despeckling process is realizedthrough solution of the multi-objective optimization problem in which the mean backscatterpower is reconstructed. The experimental results demonstrate that the proposed algorithm hasgood de-speckling capability and advantages of enhancing image details.Chapter four: In connection with the sparseness problems of target in SAR images,(1) theautomatic target detection algorithm due to the inherent sparsity of target in SAR image isproposed. Firstly, a two-dimensional discrete cosine transform dictionary is constructed toproject the SAR image into a high dimensional space and a sparse representation set of imagelocal features is achieved. Secondly, random sampling matrix is used to do compressionsampling and mean shift algorithm is applied to handle multiple sets of sample data withparallel processing. Finally, the algorithm achieves the target pixels and background pixelsclassification using the sign test method. The experimental results demonstrate that theproposed algorithms have a good target segmentation results for hard target in SAR images;(2)from the point of view of the inverse problem, we introduce a new method for target detectionin SAR images using point scattering center model based on the target backscattercharacteristics. For this algorithm, the image is projected onto frequency-aspect space throughscattering center model, giving an adaptive sparse representation. Random matrix is taken asmeasurement matrix to realize generation of the feature space. And then, the final targetdetection is realized by clustering algorithm and probabilistic voting method, achieving thereconstruction of target regional information. The experimental results demonstrate that theproposed algorithms have a good target detection results and also have a good robustness onthe speckle noise.Chapter five: In connection with the robustness problems in change detection of SAR images,(1) we introduce a new method for change detection in remote sensing images using sparserepresentstion. For the algorithm, a large collection of image patches is projected onto highdimensional spaces through improved K-SVD dictionary, giving a sparse representation pereach image patch. Random matrix is taken as measurement matrix to realize feature spacedimension reduction. And then, the final change detection is realized by clustering the featurevector space using the Fuzzy Clustering algorithm, achieving the reconstruction of changeregional information. The experimental results demonstrate that the proposed algorithms havea good change detection results both in contour and region and also have a good robustness onthe noise;(2) we introduce a new framework for two-dimensional compressed sensing and a new method for change detection in remote sensing images using two-dimensionalcompressed sensing. For the change detection algorithm, a large collection of image patches isprojected onto high dimensional spaces through improved overcomplete dictionary, giving asparse representation per each image patch. Two random matries are taken as measurementmatrix to realize feature space dimension reduction. And then, the final change detection isrealized by clustering the feature vector space using the Fuzzy Clustering algorithm, achievingthe reconstruction of change regional information. The experimental results demonstrate thatthe proposed algorithms have a good change detection results and also have a good robustnesson the noise.