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In this paper, an improved scale-invariant feature transform (SIFT) algorithm for synthetic aperture radar (SAR) image matching is proposed. Initially, feature descriptors based on gradient ratio (GR) are constructed by utilizing traditional SIFT method. In order to measure the matching degree between images, the similarities of the descriptors are then calculated via the symmetry kullback-leibler...
LBP operator shows good performance on rotation invariant while LGRPH is robust to multiplicative noise and gradient changes. In order to combine merits of both operators, an improved rotation invariant feature for SAR image is proposed in this paper. Experiments on SAR images demonstrate that the proposed feature has a good performance on targets recognition and image texture patches matching with...
In this letter, a simple, yet very powerful local descriptor called local pattern descriptor (LPD) is proposed for synthetic aperture radar (SAR) images classification. The descriptor aims at exploiting the underlying properties of SAR image texture. Specifically, LPD consists of two parts: image quantization and statistical features extraction. The method of image quantization is based on recent...
In this paper, we propose a theoretically new and effective feature for SAR image classification. The new feature combines traditional gray level co-occurrence matrix (GLCM) textural feature and the recent multilevel local pattern histogram (MLPH) feature. It can not only describe intrinsic property of land-cover/land-use surfaces, corresponding to textural information, but it also captures both local...
Aiming at the classification problem of Synthetic aperture radar (SAR) images, a classifier based on image intensity and structure is constructed. To overcome the disadvantages of conventional template matching algorithms, similarity between two images is calculated by Hausdorff function, which can handle the distortions and pixel perturbations. The function is then fed into Support vector machines...
Maximum average correlation height (MACH) filter is formulated by linearly combining the training data, which is statistically optimum and fairly robust to for finding targets in clutter when the Gaussian assumption holds. This paper proposes a nonlinear extension to the MACH filter by correntropy function which can induce a new feature space. Thus it is possible to construct linear filter equations...
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