Improved Class-Specific Codebook with Two-Step Classification for Scene-Level Classification of High Resolution Remote Sensing Images

With the rapid advances in sensors of remote sensing satellites, a large number of high-resolution images (HRIs) can be accessed every day. Land use classification using high-resolution images has become increasingly important as it can help to overcome the problems of haphazard, deteriorating environmental quality, loss of prime agricultural lands, and destruction of important wetlands, and so on. Recently, local feature with bag-of-words (BOW) representation has been successfully applied to land-use scene classification with HRIs. However, the BOW representation ignores information from scene labels, which is critical for scene-level land-use classification. Several algorithms have incorporated information from scene labels into BOW by calculating a class-specific codebook from the universal codebook and coding a testing image with a number of histograms. Those methods for mapping the BOW feature to some inaccurate class-specific codebooks may increase the classification error. To effectively solve this problem, we propose an improved class-specific codebook using kernel collaborative representation based classification (KCRC) combined with SPM approach and SVM classifier to classify the testing image in two steps. This model is robust for categories with similar backgrounds. On the standard Land use and Land Cover image dataset, the improved class-specific codebook achieves an average classification accuracy of 93% and demonstrates superiority over other state-of-the-art scene-level classification methods.