A shape–size index extraction for classification of high resolution multispectral satellite images

Abstract

We propose a new spatial feature extraction method for supervised classification of satellite images with high spatial resolution. The proposed shape–size index (SSI) feature combines homogeneous areas using spectral similarity between one central pixel and its neighbouring pixels. A spatial index considers the shape and size of the homogeneous area, and suitable spatial features are parametrically selected. The generated SSI feature is integrated with the original high resolution multispectral bands to improve the overall classification accuracy. A support vector machine (SVM) is employed as a classifier. In order to evaluate the proposed feature extraction method, KOMPSAT-2 (Korea Multipurpose Satellite 2), QuickBird-2 and IKONOS-2 high resolution satellite images are used. The experiments show that the SSI algorithm leads to a notable increase in classification accuracy over the grey level co-occurrence matrix (GLCM) and pixel shape index (PSI) algorithms, and an increase when compared with using multispectral bands only.