  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.
