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This paper considers edge interval estimation between two regions of a synthetic aperture radar (SAR) image, which differ in texture. This is a difficult task because SAR images are contaminated with speckle noise. Different point estimation strategies under multiplicative noise are discussed in the literature. It is important to assess the quality of such point estimates and to also perform inference under a given confidence level. This can be achieved through interval parameter estimation. To that end, we propose bootstrap-based edge confidence interval. The relative merits of the different inference strategies are compared using Monte Carlo simulation. The results show that interval edge estimation can be used to assess the accuracy of an edge point estimate. They also show that interval estimates can be quite accurate and that they can indicate the absence of an edge. In order to illustrate interval edge estimation, we also analyze a real data set. [ABSTRACT FROM PUBLISHER]

Polarimetric synthetic aperture radar (PolSAR) data are used extensively for terrain classification applying SAR features from various target decompositions and certain textural features. However, one source of information has so far been neglected from PolSAR classification: Color. It is a common practice to visualize PolSAR data by color coding methods and thus, it is possible to extract powerful color features from such pseudocolor images so as to provide additional data for a superior terrain classification. In this paper, we first review previous attempts for PolSAR classifications using various feature combinations and then we introduce and perform in-depth investigation of the application of color features over the Pauli color-coded images besides SAR and texture features. We run an extensive set of comparative evaluations using 24 different feature set combinations over three images of the Flevoland- and the San Francisco Bay region from the RADARSAT-2 and the AIRSAR systems operating in C- and L-bands, respectively. We then consider support vector machines and random forests classifier topologies to test and evaluate the role of color features over the classification performance. The classification results show that the additional color features introduce a new level of discrimination and provide noteworthy improvement in classification performance (compared with the traditionally employed PolSAR and texture features) within the application of land use and land cover classification. [ABSTRACT FROM PUBLISHER]

In this paper, an efficient algorithm for texture recognition of synthetic aperture radar (SAR) images is developed based on wavelet transform as a feature extraction tool and support vector machine (SVM) as a classifier. SAR image segmentation is an important step in texture recognition of SAR images. SAR images cannot be segmented successfully by using traditional methods because of the existence of speckle noise in SAR images. The algorithm, proposed in this paper, extracts the texture feature by using wavelet transform; then, it forms a feature vector composed of kurtosis value of wavelet energy feature of SAR image. In the next step, segmentation of different textures is applied by using feature vector and level set function. At last, an SVM classifier is designed and trained by using normalized feature vectors of each region texture. The testing sets of SAR images are segmented by this trained SVM. Experimental results on both agricultural and urban SAR images show that the proposed algorithm is effective for classification of different textures in SAR images, and it is also insensitive to the intensity. [ABSTRACT FROM AUTHOR]