Deep Learning For Image Search And Retrieval In Large Remote Sensing Archives

Abstract

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.

Stats

• citations26
• S2 citations—
• github stars0
• HF likes0
• heat score10.74
• arxiv keysumbul2020deep

Related papers

• Deep Hashing Learning For Visual And Semantic Retrieval Of Remote Sensing Images (2019)13.55
• Exploiting Deep Features For Remote Sensing Image Retrieval: A Systematic Investigation (2017)14.47
• CBIR Using Features Derived By Deep Learning (2020)12.02
• Metric-learning Based Deep Hashing Network For Content Based Retrieval Of Remote Sensing Images (2019)13.93
• Deep Unsupervised Contrastive Hashing For Large-scale Cross-modal Text-image Retrieval In Remote Sensing (2022)0.00
• Asymmetric Hash Code Learning For Remote Sensing Image Retrieval (2022)11.76
• A Decade Survey Of Content Based Image Retrieval Using Deep Learning (2020)18.33
• Advancements In Content-based Image Retrieval: A Comprehensive Survey Of Relevance Feedback Techniques (2023)0.00