Hashing is very popular for remote sensing image search. This article proposes a multiview hashing with learnable parameters to retrieve the queried images for a large-scale remote sensing dataset. Existing methods always neglect that real-world remote sensing data lies on a low-dimensional manifold embedded in high-dimensional ambient space. Unlike previous methods, this article proposes to learn the consensus compact codes in a view-specific low-dimensional subspace. Furthermore, we have added a hyperparameter learnable module to avoid complex parameter tuning. In order to prove the effectiveness of our method, we carried out experiments on three widely used remote sensing data sets and compared them with seven state-of-the-art methods. Extensive experiments show that the proposed method can achieve competitive results compared to the other method.

翻译：哈希方法在高分辨率遥感图像检索中非常流行。本文提出了一种具有可学习参数的多视图哈希方法，用于在大规模遥感数据集中检索查询图像。现有方法常常忽略真实遥感数据位于嵌入高维空间的低维流形上这一事实。与以往方法不同，本文提出在视图特定的低维子空间中学习共识紧凑编码。此外，我们添加了一个超参数可学习模块以避免复杂的参数调优。为了证明我们方法的有效性，我们在三个广泛使用的遥感数据集上进行了实验，并与七种最先进的方法进行了比较。大量实验表明，所提方法相比其他方法能够取得具有竞争力的结果。