Hyperspectral target detection method based on spatial-spectral joint weighted dictionary learning with online updating mechanism

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In recent years, many target detection methods for hyperspectral images based on sparse representation (SR) theory have been proposed and achieved good results. However, these methods still have some deficiencies. Specifically, these methods usually give the same weight to the pixels in the neighborhood when they use the spatial information, and obtain the spectral average features to replace the spectral features of the central pixel. However, the influence of each pixel in the neighborhood of the central pixel may be different. In addition, when the dictionary learning method is used for target detection, its performance depends on the constructed dictionary. To solve these problems, an innovative target detection method based on spatial-spectral joint weighted dictionary learning with an online updating mechanism is proposed. First of all, different from traditional SR method, this method integrates the spatial adaptive neighborhood information into the sparse coding stage to realize the spatial-spectral weighted joint SR. Second, the online dictionary updating mechanism and a two-step optimization method are used together to update the dictionary and sparse coding coefficients alternately, which can learn the optimal dictionary set in real time. The experimental results show that the proposed method has better performance when compared with four state-of-the-art dictionary learning-based methods for hyperspectral target detection.