Abstract:

We propose a remote sensing target recognition method based on the extended dictionary and sparse representation classification to solve problems such as poor visual contrast, low resolution, and rotation in different angles. Firstly, the training and test samples are enhanced with dyadic wavelet transform. Secondly, a feature dictionary is constituted by extracting SIFT features from the enhanced images. We then compose an extended dictionary which contains both an original training dictionary and a feature dictionary for sparse representation, so that the extended dictionary can be more discriminative, and the recognition rate can be higher. We also analyze the influence of SIFT features after random projection on the recognition rate. Experimental results show that the method is robust to the recognition of remote sensing targets.

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