Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images

Abstract

Hashing methods have been recently found very effective in retrieval of remote sensing (RS) images due to their computational efficiency and fast search speed. The traditional hashing methods in RS usually exploit hand-crafted features to learn hash functions to obtain binary codes, which can be insufficient to optimally represent the information content of RS images. To overcome this problem, in this paper we introduce a metric-learning based hashing network, which learns: 1) a semantic-based metric space for effective feature representation; and 2) compact binary hash codes for fast archive search. Our network considers an interplay of multiple loss functions that allows to jointly learn a metric based semantic space facilitating similar images to be clustered together in that target space and at the same time producing compact final activations that lose negligible information when binarized. Experiments carried out on two benchmark RS archives point out that the proposed network significantly improves the retrieval performance under the same retrieval time when compared to the state-of-the-art hashing methods in RS.

arXiv commentAccepted to IEEE Geoscience and Remote Sensing Letters. For code visit: https://github.com/MLEnthusiast/MHCLN

References in corpus (4)

• FaceNet: A Unified Embedding for Face Recognition and Clustering
• AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification
• Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks
• Feature Learning based Deep Supervised Hashing with Pairwise Labels

Cited by in corpus (5)

• BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval
• SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition
• A Novel Graph-Theoretic Deep Representation Learning Method for Multi-Label Remote Sensing Image Retrieval
• A Novel Framework to Jointly Compress and Index Remote Sensing Images for Efficient Content-Based Retrieval
• Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval