Multiple resolution block feature for remote-sensing scene classification

Abstract

ABSTRACT Great efforts have been devoted to improving the performance of scene classification. However, it is still a challenging task because of the complex background and diverse objects in scene images. To address this issue, multiple resolution block feature (MRBF) is proposed for remote-sensing scene classification. It is a unified and effective scene representation, consisting of completed double cross pattern (CDCP) combined with fisher vectors (FV). Specifically, in order to capture more robust and richer scene information, multiple resolution block descriptor is devised based on CDCP. After that, it is combined with FV to construct unified MRBF, which can fully exploit discriminative information from the block descriptor. Finally, the scene classification is achieved by kernel extreme learning machine. Extensive evaluations on four benchmark scene data-sets demonstrate the effectiveness and superiority of the proposed MRBF method for scene classification.