Multi-object Classification of Remote Sensing Image Based on Affine-invariant Supervised Discrete Hashing

The multi-object classification of remote sensing images has been a challenging task. Firstly, due to the complexity of the data and the high requirement of storage, the traditional classification methods are difficult to achieve both the accuracy and speed of the classification. Secondly, the affine transformation caused by the remote sensing imaging process, the real-time performance of the object interpretation is difficult to be realized. To solve the problem, a multi-object classification of remote sensing image is proposed based on affine-invariant discrete hashing (AIDH). This method uses supervised discrete hashing with the advantage of low storage and high efficiency, jointed with affine-invariant factor, to construct affine-invariant discrete hashing. By constraining the affine transform samples with the same semantic information to the similar binary code space, the method achieves the enhancement on classification precision. Experiments show that under the two datasets of NWPU VHR-10 and RSDO-dataset, the method presented in this paper is more efficient than classical hash method and classification method, and it is also guaranteed in accuracy. © Copyright 2019, Institute of Software, the Chinese Academy of Sciences. All rights reserved.