Commenced in January 2007
Paper Count: 30011
A Hybrid Image Fusion Model for Generating High Spatial-Temporal-Spectral Resolution Data Using OLI-MODIS-Hyperion Satellite Imagery
Abstract:Spatial, Temporal, and Spectral Resolution (STSR) are three key characteristics of Earth observation satellite sensors; however, any single satellite sensor cannot provide Earth observations with high STSR simultaneously because of the hardware technology limitations of satellite sensors. On the other hand, a conflicting circumstance is that the demand for high STSR has been growing with the remote sensing application development. Although image fusion technology provides a feasible means to overcome the limitations of the current Earth observation data, the current fusion technologies cannot enhance all STSR simultaneously and provide high enough resolution improvement level. This study proposes a Hybrid Spatial-Temporal-Spectral image Fusion Model (HSTSFM) to generate synthetic satellite data with high STSR simultaneously, which blends the high spatial resolution from the panchromatic image of Landsat-8 Operational Land Imager (OLI), the high temporal resolution from the multi-spectral image of Moderate Resolution Imaging Spectroradiometer (MODIS), and the high spectral resolution from the hyper-spectral image of Hyperion to produce high STSR images. The proposed HSTSFM contains three fusion modules: (1) spatial-spectral image fusion; (2) spatial-temporal image fusion; (3) temporal-spectral image fusion. A set of test data with both phenological and land cover type changes in Beijing suburb area, China is adopted to demonstrate the performance of the proposed method. The experimental results indicate that HSTSFM can produce fused image that has good spatial and spectral fidelity to the reference image, which means it has the potential to generate synthetic data to support the studies that require high STSR satellite imagery.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132545Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 Tong, X., Zhao, W., Xing, J., & Fu, W. (2016, July). Status and development of China High-Resolution Earth Observation System and application. In Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International (pp. 3738-3741). IEEE.
 The future missions of NASA’s Earth Observing System, https://eospso.nasa.gov/future-missions.
 Zhang, Y. (2004). Understanding image fusion. Photogrammetric engineering and remote sensing, 70(6), 657-661.
 Gao, F., Masek, J., Schwaller, M., & Hall, F. (2006). On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. Geoscience and Remote Sensing, IEEE Transactions on, 44(8), 2207-2218.
 Zhang, Y. (1999). A new merging method and its spectral and spatial effects. International Journal of Remote Sensing, 20(10), 2003-2014.
 Winter, M. E., Winter, E. M., Beaven, S. G., & Ratkowski, A. J. (2007, March). Hyperspectral image sharpening using multispectral data. In Aerospace Conference, 2007 IEEE (pp. 1-9). IEEE.
 Song, H., Huang, B., Zhang, K., & Zhang, H. (2014). Spatio-spectral fusion of satellite images based on dictionary-pair learning. Information Fusion, 18, 148-160.
 Huang, B., & Song, H. (2012). Spatiotemporal reflectance fusion via sparse representation. Geoscience and Remote Sensing, IEEE Transactions on, 50(10), 3707-3716.
 Song, H., & Huang, B. (2013). Spatiotemporal satellite image fusion through one-pair image learning. IEEE Transactions on Geoscience and Remote Sensing, 51(4), 1883-1896.
 Zhao, Y., & Huang, B. (2016, August). A Two-step Spatio-Temporal satellite image Fusion Model for temporal changes of various LULC under one-pair prior images scenario. In Signal Processing, Communications and Computing (ICSPCC), 2016 IEEE International Conference on (pp. 1-5). IEEE.
 Shen, H., Meng, X., & Zhang, L. (2016). An Integrated Framework for the Spatio–Temporal–Spectral Fusion of Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 54(12), 7135-7148.
 Huang, B., Zhang, H., Song, H., Wang, J., & Song, C. (2013). Unified fusion of remote-sensing imagery: generating simultaneously high-resolution synthetic spatial–temporal–spectral earth observations. Remote sensing letters, 4(6), 561-569.
 Zhang, L., Fu, D., Sun, X., Chen, H., & She, X. (2016, April). A spatial-temporal-spectral blending model using satellite images. In IOP Conference Series: Earth and Environmental Science (Vol. 34, No. 1, p. 012042). IOP Publishing.
 Gevaert, C. M., & García-Haro, F. J. (2015). A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion. Remote sensing of Environment, 156, 34-44.
 Zhu, X., Helmer, E. H., Gao, F., Liu, D., Chen, J., & Lefsky, M. A. (2016). A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sensing of Environment, 172, 165-177.
 Zhang, Y. (2008). U.S. Patent No. 7,340,099. Washington, DC: U.S. Patent and Trademark Office.