Unsupervised hashing methods have attracted considerable attention in large-scale remote sensing (RS) image retrieval, due to their capability for massive data processing with significantly reduced storage and computation. Although existing unsupervised hashing methods are suitable for operational applications, they exhibit limitations when accurately modeling the complex semantic content present in RS images using binary codes (in an unsupervised manner). To address this problem, in this letter, we introduce a novel unsupervised hashing method that takes advantage of the generative nature of probabilistic topic models to encapsulate the hidden semantic patterns of the data into the final binary representation. Specifically, we introduce a new probabilistic latent semantic hashing (pLSH) model to effectively learn the hash codes using three main steps: 1) data grouping, where the input RS archive is clustered into several groups; 2) topic computation, where the pLSH model is used to uncover highly descriptive hidden patterns from each group; and 3) hash code generation, where the data probability distributions are thresholded to generate the final binary codes. Our experimental results, obtained on two benchmark archives, reveal that the proposed method significantly outperforms state-of-the-art unsupervised hashing methods.

中文翻译：

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使用概率潜在语义哈希的无监督遥感图像检索

无监督散列方法在大规模遥感 (RS) 图像检索中引起了相当大的关注，因为它们能够处理大量数据并显着减少存储和计算。尽管现有的无监督散列方法适用于操作应用，但在使用二进制代码（以无监督方式）对 RS 图像中存在的复杂语义内容进行准确建模时，它们表现出局限性。为了解决这个问题，在这封信中，我们引入了一种新的无监督散列方法，该方法利用概率主题模型的生成特性将数据的隐藏语义模式封装到最终的二进制表示中。具体来说，我们引入了一种新的概率潜在语义散列 (pLSH) 模型，使用三个主要步骤来有效地学习散列码：1) 数据分组，其中输入 RS 档案被聚类成几个组；2) 主题计算，其中 pLSH 模型用于从每个组中发现高度描述性的隐藏模式；和 3) 哈希码生成，其中数据概率分布被阈值化以生成最终的二进制代码。我们在两个基准档案上获得的实验结果表明，所提出的方法明显优于最先进的无监督散列方法。其中数据概率分布被阈值化以生成最终的二进制代码。我们在两个基准档案上获得的实验结果表明，所提出的方法明显优于最先进的无监督散列方法。其中数据概率分布被阈值化以生成最终的二进制代码。我们在两个基准档案上获得的实验结果表明，所提出的方法明显优于最先进的无监督散列方法。