Fully Connected Hashing Neural Networks for Indexing Large-Scale Remote Sensing Images

Na Liu, Haiming Mou, Jun Tang, Lihong Wan, Qingdu Li and Ye Yuan ()
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Na Liu: Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China
Haiming Mou: Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China
Jun Tang: Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China
Lihong Wan: Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China
Qingdu Li: Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China
Ye Yuan: Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China

Mathematics, 2022, vol. 10, issue 24, 1-15

Abstract: With the emergence of big data, the efficiency of data querying and data storage has become a critical bottleneck in the remote sensing community. In this letter, we explore hash learning for the indexing of large-scale remote sensing images (RSIs) with a supervised pairwise neural network with the aim of improving RSI retrieval performance with a few binary bits. First, a fully connected hashing neural network (FCHNN) is proposed in order to map RSI features into binary (feature-to-binary) codes. Compared with pixel-to-binary frameworks, such as DPSH (deep pairwise-supervised hashing), FCHNN only contains three fully connected layers and incorporates another new constraint, so it can be significantly accelerated to obtain desirable performance. Second, five types of image features, including mid-level and deep features, were investigated in the learning of the FCHNN to achieve state-of-the-art performances. The mid-level features were based on Fisher encoding with affine-invariant local descriptors, and the deep features were extracted by pretrained or fine-tuned CNNs (e.g., CaffeNet and VGG-VD16). Experiments on five recently released large-scale RSI datasets (i.e., AID, NWPU45, PatternNet, RSI-CB128, and RSI-CB256) demonstrated the effectiveness of the proposed method in comparison with existing handcrafted or deep-based hashing methods.

Keywords: neural network; binary hash code; image retrieval; remote sensing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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