SAR image target identification method based on enhanced kernel sparse representation

Abstract

The invention discloses an SAR image target identification method based on enhanced kernel sparse representation, and the method comprises the steps: firstly, providing a multi-scale unicast feature extraction method which is used for simultaneously extracting the spatial and frequency domain information of an SAR image target; secondly, designing a classifier based on enhanced kernel sparse representation for target identification. The method is different from a traditional kernel sparse representation classifier. The designed classifier based on enhanced kernel sparse representation firstlyadopts kernel principal component analysis (Kernel Princture Analytics), and then the kernel Princture Analytics is used as the main component of the classifier. The method comprises the following steps of: calculating an enhanced pseudo-transformation matrix by using KPCA (kernel Fisher Discriminant Analytics) and KFDA (Kernel Fisher Discriminant Analytics); secondly, proposing a discriminative feature mapping method based on an enhanced pseudo-transformation matrix, and carrying out dimension reduction on the features in a kernel space; And finally, calculating a sparse coefficient by minimizing an L1 norm, and identifying the target category based on an error of sparse reconstruction. According to the method, the SAR target is identified based on the multi-scale unicast signal theory and the enhanced kernel sparse representation classifier, and a good classification and identification effect can be achieved.

Description

technical field [0001] The invention belongs to the field of image processing and pattern recognition, in particular to a new SAR image target recognition method based on enhanced kernel sparse representation. Background technique [0002] Synthetic Aperture Radar (SAR) is a high-resolution imaging radar. Compared with optical and infrared radars, its imaging is less limited by weather and other conditions. It has all-weather, all-weather, multi-view and Features such as high resolution. Target recognition based on SAR images has very important applications in various military and civilian fields, such as military battlefield reconnaissance, ground attack, civilian topographic mapping, ocean observation, disaster forecast, crop evaluation, etc. In-depth research has important theoretical significance and practical value. [0003] At present, target recognition methods for SAR images are generally divided into three categories: template matching methods, model-based methods...

AI Technical Summary

Problems solved by technology

[0004] Although the existing algorithms can deal with the problem of SAR image target recognition, their performance still needs to be further improved: (1) At the current stage of SAR target recognition, there is a lack of complete, compact and discriminative feature extraction methods; (2) ) How to design a robust and reliable classifier is a difficulty when the extracted multiple types of features are not linearly separable

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