Learning Source-Invariant Deep Hashing Convolutional Neural Networks for Cross-Source Remote Sensing Image Retrieval

• Published in: IEEE transactions on geoscience and remote sensing 2018-11, Vol.56 (11), p.6521-6536
• Main Authors: Li, Yansheng, Zhang, Yongjun, Huang, Xin, Ma, Jiayi
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Big Data; Complexity theory; Convolutional neural networks; Cross-source large-scale remote sensing image retrieval (CS-LSRSIR); Data; Data analysis; Data management; Data processing; Detection; dual-source remote sensing image data set (DSRSID); Exploitation; Image management; Image processing; Image retrieval; Invariants; Land cover; Learning systems; Motivation; Neural networks; Optimization; Remote sensing; remote sensing big data (RSBD) management and mining; source-invariant deep hashing convolutional neural networks (SIDHCNNs)
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2018.2839705

Due to the urgent demand for remote sensing big data analysis, large-scale remote sensing image retrieval (LSRSIR) attracts increasing attention from researchers. Generally, LSRSIR can be divided into two categories as follows: uni-source LSRSIR (US-LSRSIR) and cross-source LSRSIR (CS-LSRSIR). More specifically, US-LSRSIR means the inquiry remote sensing image and images in the searching data set come from the same remote sensing data source, whereas CS-LSRSIR is designed to retrieve remote sensing images with a similar content to the inquiry remote sensing image that are from a different remote sensing data source. In the literature, US-LSRSIR has been widely exploited, but CS-LSRSIR is rarely discussed. In practical situations, remote sensing images from different kinds of remote sensing data sources are continually increasing, so there is a great motivation to exploit CS-LSRSIR. Therefore, this paper focuses on CS-LSRSIR. To cope with CS-LSRSIR, this paper proposes source-invariant deep hashing convolutional neural networks (SIDHCNNs), which can be optimized in an end-to-end manner using a series of well-designed optimization constraints. To quantitatively evaluate the proposed SIDHCNNs, we construct a dual-source remote sensing image data set that contains eight typical land-cover categories and 10 000 dual samples in each category. Extensive experiments show that the proposed SIDHCNNs can yield substantial improvements over several baselines involving the most recent techniques.