Hyperspectral target detection: A new method based on learned dictionary

Sparse representation has been introduced to tackle the target detection problem in hyperspectral imagery. While using windows to build the sparse dictionary, there exists target contamination problem. In our approach, we utilize a learning method based on convex optimization to build a dictionary for sparse target detection. Through its application, prior information such as the size of windows can be spared, while considerably reducing the occurrence of contamination. To verify the efficacy of using the learned dictionary, the dictionary built through the dual-window method is used as a comparison and two sparse target detection methods are employed afterward. Experimental results show that, by using the learned dictionary, a better result is obtained compared to the methods using traditional dual-window background dictionary.