Multiple resolution block feature for remote-sensing scene classification

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.

Acknowledgments

The authors would like to thank Shawn Newsam, Gui-Song Xia and Junwei Han, who generously provided their UCM data-set, WHU-RS19 and AID data-sets, NWPU-RESISC45 data-set. The authors would like to thank the editors and anonymous reviewers for their valuable comments and helpful suggestions, which greatly improved the quality of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [61201323]; Natural Science Foundation projects of Shaanxi Province [2017JM6026].