论文标题
大型遥感档案中的图像搜索和检索深度学习
Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives
论文作者
论文摘要
本章介绍了遥感（RS）中基于内容的图像搜索和检索（CBIR）系统的最新进展，以从大量数据档案中发现快速准确的信息。最初，我们分析了依靠手工制作的RS图像描述符的传统CBIR系统的局限性。然后，我们将注意力集中在RS CBIR系统的进步上，深度学习（DL）模型处于最前沿。特别是，我们介绍了最新的基于DL的CBIR系统的理论特性，用于表征RS图像的复杂语义内容。在讨论了它们的优势和局限性之后，我们介绍了基于深哈希的CBIR系统，这些系统具有巨大的数据档案中具有较高时间效率的搜索能力。最后，讨论了RS CBIR中最有希望的研究方向。
This chapter presents recent advances in content based image search and retrieval (CBIR) systems in remote sensing (RS) for fast and accurate information discovery from massive data archives. Initially, we analyze the limitations of the traditional CBIR systems that rely on the hand-crafted RS image descriptors. Then, we focus our attention on the advances in RS CBIR systems for which deep learning (DL) models are at the forefront. In particular, we present the theoretical properties of the most recent DL based CBIR systems for the characterization of the complex semantic content of RS images. After discussing their strengths and limitations, we present the deep hashing based CBIR systems that have high time-efficient search capability within huge data archives. Finally, the most promising research directions in RS CBIR are discussed.
