Recently, with the number of large-scale remote sensing (RS) images increasing, the demand for large-scale RS image object classification is growing, and many researchers are interested. Hashing, as a result of its low memory requirements and high time efficiency, has widely solved the problem of large-scale RS images. Supervised hashing methods mainly leverage RS image label information to learn hashing function; however, the similarity of the original feature space cannot be well preserved, which cannot meet the accurate requirements of RS images object classification. To address the aforementioned analysis, we propose a method named optimized projection supervised discrete hashing (OPSDH), which jointly learns optimized projection constraint and discrete binary codes generation model. It uses an effective optimized projection method to further constraint on the supervised hashing learn, and generated hash codes preserve the similarity based on the data label while retaining the original feature space’s similarity. The experimental results show that OPSDH reaches improved performance compared with existing hashing methods and demonstrates that the proposed OPSDH is more efficient for operational applications.

中文翻译：

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基于优化投影监督离散哈希的遥感图像目标分类

近年来，随着大规模遥感（RS）图像数量的增加，对大规模RS图像对象分类的需求正在增长，并且许多研究人员对此感兴趣。由于其低存储需求和高时间效率的结果，散列已广泛解决了大规模RS图像的问题。有监督的哈希方法主要利用RS图像标签信息来学习哈希功能。然而，原始特征空间的相似性不能很好地保留，无法满足RS图像目标分类的准确要求。为了解决上述分析问题，我们提出了一种称为优化投影监督离散哈希（OPSDH）的方法，该方法可以共同学习优化投影约束和离散二进制代码生成模型。它使用有效的优化投影方法进一步约束了监督的哈希学习，并且生成的哈希码在保留原始特征空间相似性的同时，基于数据标签保留了相似性。实验结果表明，与现有的哈希算法相比，OPSDH的性能有所提高，并且表明所提出的OPSDH对于操作应用程序更为有效。