Remote sensing image classification based on multiple morphological component analysis

In this work, we propose a new multiple morphological component analysis (MMCA) based decomposition framework for remote sensing image classification. The proposed MMCA framework aims at exploiting relevant textural characteristics present in a scene such as content, coarseness, contrast or directionality. Specifically, MMCA decomposes an image into a pair of morphological components (for each textural characteristic), which can be associated to a smooth and a textural components. The extracted features are then used for classification with a multinomial logistic regression (MLR). The experimental results, conducted using both a hyperspectral and a synthetic aperture radar (SAR) images, reveal that the proposed scheme can lead to state-of-the-art classification accuracy.