Hyperspectral image interpretation based on partial least squares

Remote sensed hyperspectral images have been used for many purposes and have become one of the most important tools in remote sensing. Due to the large amount of available bands, e.g., a few hundreds, the feature extraction step plays an important role for hyperspectral images interpretation. In this paper, we extend a well-know feature extraction method called Extended Morphological Profile (EMP) which encodes spatial and spectral information by using Partial Least Squares (PLS) to emphasize the importance of the more discriminative features. PLS is employed twice in our proposal, i.e., to the EMP features and to the raw spatial information, which are then concatenated to be further interpreted by the SVM classifier. Our experiments in two well-known data sets, the Indian Pines and Pavia University, have shown that our proposal outperforms the accuracy of classification methods employing EMP and other baseline feature extraction methods with different classifiers.