Abstract
Abstract 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, called kernel-GNDE (KGNDE). The experiment in this paper shows that the proposed algorithms have better recognition performance than PCA and KPCA.
Highlights
Synthetic aperture radar (SAR) has been widely used in many fields, such as terrain surveying, marine monitoring, and earth observation, because of its all-time, allweather, penetrating ability and high resolution
The procedure of synthetic aperture radar automatic target recognition (SAR automatic target recognition (ATR)) can be divided into four major steps: detection, discrimination, feature extraction, and recognition
The feature extraction is one of the crucial steps for SAR ATR, which can reduce the dimensionality of SAR images greatly and improve recognition efficiency
Summary
Synthetic aperture radar (SAR) has been widely used in many fields, such as terrain surveying, marine monitoring, and earth observation, because of its all-time, allweather, penetrating ability and high resolution. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used for SAR image feature extraction [1,2] because of their simplicity and effectiveness Both of them are based on a global linear structure and need to transform a two-dimensional image into a one-dimensional vector. Based on the manifold learning method, we design neighborhood geometry and target function using the average of similar dispersion of dataset, and calculate the linear embedding mapping, according to category information When this method was extended to vector space, we named it as generalized neighbor discriminant embedding (GNDE). In order to reduce calculation burden, a kernel function was employed to replace the high-dimensional vector inner product This is the kernel GNDE (KGNDE) method mainly discussed in this paper.
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