Content-based high-resolution satellite image classification

Abstract

Content-based image classification has produced successful and automated applications in various service and product industries. In this paper, high-resolution satellite scene classification based on multiple feature combination is considered. We have proposed confidence co-occurrence matrix, which is a modification of the generalized co-occurrence matrix. The proposed framework combines RGB histogram, HSV histogram, local binary pattern, confidence co-occurrence matrix properties and Canny’s edge detection approach. The approach creates a fixed-size feature vector of size 1632. Once a feature vector has been constructed, classification is performed using linear support vector machine. The system is tested using widely popular benchmark Satellite Scene dataset and UC Merced land used dataset having 19 and 21 classes respectively. The proposed system also works well in agricultural science. The system is also tested on folio dataset having 32 species of leaf. The proposed system is implemented in MATLAB and achieves an average class classification accuracy of 99%.