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This paper proposes a novel registration method for optical and SAR images which is based on straight line features and mutual information. Firstly, different edge detectors are employed to detect the line segments in both optical and SAR images respectively. Then, through the Hough transform and a straight line fitting and filtration method, the main straight lines of each image are extracted and...
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...
Most of existing change detection methods could be classified into three groups, the traditional pixel-based change detection (PBCD), the object-based change detection (OBCD), and the hybrid change detection (HCD). Nevertheless, both PBCD and OBCD have disadvantages, and classical HCD methods belong to intuitive decision-level fusion schemes of PBCD and OBCD. There is no optimum HCD method as of yet...
The real-time ability and recognition rate are two primary goals for evaluating the performance of an SAR image target recognition system. This paper concentrates on the analysis of key factors which influence these two goals. According to the analysis, a fast SAR target recognition approach is proposed, which utilizes a self-organizing neural network trained with the Hebbian rule to extract the principal...
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