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.