摘要:遥感图像的多目标分类是一个具有挑战性的课题.首先，由于数据的复杂性以及算法对存储的高需求，传统分类方法很难兼顾到分类的精度和速度；其次，遥感成像过程中产生的仿射变换，使得目标的快速解译难以实现.为此，提出一种基于仿射不变离散哈希（AIDH）的遥感图像多目标分类方法.该方法采用具有低存储、高效率优势的监督离散哈希框架，结合仿射不变优化因子，构造仿射不变离散哈希，通过将具有相同语义信息的仿射变换样本约束到相似的二值码空间实现分类精度的提高.实验结果表明，在NWPU VHR-10和RSDO-dataset两个数据集下，相比于经典的哈希方法和分类方法，所提方法在具备了高效性的同时，其精度也得到了保证.



Abstract: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.



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