ZHANG Aizhu1,2(), WANG Wei1, ZHENG Xiongwei3, YAO Yanjuan4, SUN Genyun1,2(), XIN Lei5, WANG Ning5, HU Guang6
1. College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China 2. Laboratory for Marine Resources Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237,China 3. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083,China 4. Satellite Environment Center, Ministry of Environmental protection of China, Beijing 100094, China 5. North China Sea Marine Forecasting Center of State Oceanic Administration, Qingdao 266061,China 6. Wuhan SunMap RS Techology Co., Ltd., Wuhan 430223, China
The temporal resolution of high spatial resolution remote sensing data can be effectively improved by spatio-temporal fusion of remote sensing data. However, the most widely used spatial and temporal adaptive reflectance fusion model (STARFM) fails to achieve highly accurate prediction effects for areas with abrupt changes at present. Given this, this paper proposed a hierarchical spatial-temporal fusion model (H-STFM). In this model, the target pixels to be predicted are divided into pixels with phenological change and pixels with abrupt changes, which are predicted using linear regression and weighted filtering methods, respectively. Then the prediction results of the two types of pixels are fused using an optimized time weighted function to form the final prediction map. The H-STFM proposed in this paper was qualitatively and quantitatively assessed using two sets of medium-resolution remote sensing images from moderate resolution imaging spectrometer (MODIS) and Landsat satellite. As indicated by the experiment results, H-STFM is significantly superior to STARFM in terms of structural similarity and relative dimensionless global error.
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2021, Vol. 33 
), 王伟1, 郑雄伟3, 姚延娟4, 孙根云1,2(
