Deep-hashing methods have drawn significant attention during the past years in the field of remote sensing (RS) owing to their prominent capabilities for capturing the semantics from complex RS scenes and generating the associated hash codes in an end-to-end manner. Most existing deep-hashing methods exploit pairwise and triplet losses to learn the hash codes with the preservation of semantic-similarities which require the construction of image pairs and triplets based on supervised information (e.g., class labels). However, the learned Hamming spaces based on these losses may not be optimal due to an insufficient sampling of image pairs and triplets for scalable RS archives. To solve this limitation, we propose a new deep-hashing technique based on the class-discriminated neighborhood embedding, which can properly capture the locality structures among the RS scenes and distinguish images class-wisely in the Hamming space. An extensive experimentation has been conducted in order to validate the effectiveness of the proposed method by comparing it with several state-of-the-art conventional and deep-hashing methods. The related codes of this article will be made publicly available for reproducible research by the community.

中文翻译：

---

基于类别区分邻域嵌入的深度哈希

深度哈希方法在过去几年中在遥感（RS）领域引起了极大的关注，因为它们具有从复杂的 RS 场景中捕获语义并以端到端的方式生成相关哈希码的突出能力。大多数现有的深度散列方法利用成对和三元组损失来学习具有语义相似性的哈希码，这需要基于监督信息（例如类标签）构建图像对和三元组。然而，由于用于可扩展 RS 档案的图像对和三元组的采样不足，基于这些损失的学习汉明空间可能不是最佳的。为了解决这个限制，我们提出了一种新的基于类别区分邻域嵌入的深度散列技术，它可以正确捕获 RS 场景中的局部结构，并在汉明空间中按类别区分图像。为了通过将其与几种最先进的传统和深度散列方法进行比较来验证所提出方法的有效性，已经进行了广泛的实验。本文相关代码将公之于众，供社区可复制研究使用。