Hyperspectral image classification by sparse representation with nonlocal adaptive dictionary

In this paper, a novel nonlocal dictionary learning method is proposed for sparse-representation-based classification (SRC) to label high-dimensional hyperspectral imagery (HSI). In SRC, the conventional dictionary is constructed using all of the training pixels, which is inefficient due to the high-dimension low-sample-size classification problem. In this paper, we construct the dictionary by adding more appropriate pixels into the dictionary. Specifically, we select the supplementaries from the neighboring pixels of the original training pixels based on the assumption that the adjacent pixels belong to the same class with a high probability, and propose an estimative function for the selection. Furthermore, this estimative function is adopted again to select the components of signal matrix in joint sparsity model (JSM) to improve classification accuracy. Experimental results have shown that the dictionary optimized using our method can achieve better classification results with substantially expanded dictionary size than only using the training pixels.