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Learning Source-Invariant Deep Hashing Convolutional Neural Networks for Cross-Source Remote Sensing Image Retrieval.
- Source :
- IEEE Transactions on Geoscience & Remote Sensing; Nov2018, Vol. 56 Issue 11, p6521-6536, 16p
- Publication Year :
- 2018
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- REMOTE sensing
IMAGE retrieval
ARTIFICIAL neural networks
LAND cover
BIG data
Subjects
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 56
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 133667579
- Full Text :
- https://doi.org/10.1109/TGRS.2018.2839705