Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 56, Issue 6, Pages 3358-3370Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2018.2798663
Keywords
Heterogeneous land surface monitoring; remote-sensing image fusion; spatio-spectral features; temporal features
Categories
Funding
- National Natural Science Foundation of China [61432014, 61772402, U1605252, 61671339, 61571343]
- National Key Research and Development Program of China [2016QY01W0200]
- Key Industrial Innovation Chain in Industrial Domain [2016KTZDGY04-02]
- National High-Level Talents Special Support Program of China [CS31117200001]
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t Different spectral, spatial, and temporal features have been widely used in the remote-sensing image analysis. The further development of multiple sensor remote-sensing technologies has made it necessary to explore new methods of remote-sensing image fusion using different optical image data sets which provide complementary image properties and a tradeoff among spatial, spectral, and temporal resolutions. However, due to problems in assessing correlations between different types of satellite data with different resolutions, a few efforts have been made to explore spatio-spectral-temporal features. For this purpose, we propose a novel sparse representation model to generate synthesized frequent high-spectral and high-spatial resolution data by blending multiple types: spatio-temporal data fusion, spectral-temporal data fusion, spatio-spectral data fusion, and spatio-spectral-temporal data fusion. The proposed method exploits high-spectral correlation across spectral domains and high self-similarity across spatial domains to learn the spatio-spectral fusion basis. Then, it associates temporal changes using a local constraint sparse representation. The integrated spatio-spectral-temporal sparse representation model based on the learned spectral-spatial and temporal change features strengthens the model's ability to provide high-resolution data needed to address demanding work in real-world applications. Finally, the proposed method is not restricted to a certain type of data, but it can associate any type of remote-sensing data and be applied to dynamic changes in heterogeneous landscapes. The experimental results illustrate the effectiveness and efficiency of the proposed method.
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