An Integrated Spatio-Spectral–Temporal Sparse Representation Method for Fusing Remote-Sensing Images With Different Resolutions

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.