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Recognizing the category attributes from the real world targets is one of the most challenging and attractive fields in synthetic aperture radar (SAR) application. It is an important issue to explore the spatial distribution characteristics of multi-aspect angle imagery in synthetic aperture radar automatic target recognition (SAR ATR). In this paper, we will research the spatial structure of multi-aspect...
In this paper, we propose a new supervised feature extraction algorithm in synthetic aperture radar automatic target recognition (SAR ATR), called generalized neighbor discriminant embedding (GNDE). Based on manifold learning, GNDE integrates class and neighborhood information to enhance discriminative power of extracted feature. Besides, the kernelized counterpart of this algorithm is also proposed,...
Feature extraction is a crucial step in synthetic aperture radar (SAR) automatic target recognition (ATR). In this paper, we propose a feature extraction method named marginal sample discriminant embedding (MSDE) which is based on manifold learning theory. This method can preserve class information and neighborhood information of original data during dimensionality reduction. It keeps neighbor relations...
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