Satellite Image Classification Using Morphological Component Analysis of Texture and Cartoon Layers

In this letter, we proposed a high-resolution satellite image classification method using morphological component analysis of texture and cartoon layers. The construction of the dictionary matrix used in the algorithm is based on independent component analysis. After the decomposition, we obtain the morphological coefficient vectors in both texture and cartoon layers which are termed as the sparse representation of the input high-resolution satellite image. By combining the features from two layers, the total probability of the target image classification is calculated out according to the maximum likelihood mechanism. Quantitative analysis on experiment results and comparisons with classic image classification algorithms have proven that the proposed classification method has better accuracy, efficiency, and performance than most of state-of-the-art classification methods.