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This chapter presents recent advances in content based image search andretrieval (CBIR) systems in remote sensing (RS) for fast and accurateinformation discovery from massive data archives. Initially, we analyze thelimitations of the traditional CBIR systems that rely on the hand-crafted RSimage descriptors. Then, we focus our attention on the advances in RS CBIRsystems for which deep learning (DL) models are at the forefront. Inparticular, we present the theoretical properties of the most recent DL basedCBIR systems for the characterization of the complex semantic content of RSimages. After discussing their strengths and limitations, we present the deephashing based CBIR systems that have high time-efficient search capabilitywithin huge data archives. Finally, the most promising research directions inRS CBIR are discussed.

Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives