Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images
This work addresses the need for efficient and accurate retrieval of remote sensing images, which is incremental as it builds on existing hashing methods by integrating metric learning and deep networks.
The paper tackles the problem of content-based retrieval of remote sensing images by introducing a metric-learning based deep hashing network that learns a semantic-based metric space and compact binary hash codes, resulting in significantly improved retrieval performance under the same retrieval time compared to state-of-the-art hashing methods.
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.