Hyperspectral Imagery Target Detection Using Collaborative Representation with Spectral Variation Extended Dictionary

Collaborative representation plays an increasingly important role in the field of hyperspectral imagery target detection, resulting in improving detection performance. It is known that, in hyperspectral imagery, both the sensor and external factors (such as weather, illumination and other environmental changes) will lead to the spectral variations within the same type of material, which may greatly affect the detection accuracy. To deal with this issue, a new target detection method using collaborative representation with spectral variation extended dictionary is proposed for hyperspectral imagery in this paper. In the proposed method, an extended dictionary is constructed by enclosing the spectral variation library into the original dictionary, and the following collaborative representation makes the atoms in both original dictionary and spectral variation library contribute to the residual estimation. Compared to the traditional collaborative representation based target detection method, the newly proposed one exhibits better detection performance.