A hierarchical spatiotemporal adaptive fusion model using one image pair
Publication in refereed journal

香港中文大學研究人員
替代計量分析
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其它資訊
摘要Image fusion techniques that blend multi-sensor characteristics to generate synthetic data with fine resolutions have generated great interest within the remote sensing community. Over the past decade, although many advances have been made in the spatiotemporal fusion models, there still remain several shortcomings in existing methods. In this article, a hierarchical spatiotemporal adaptive fusion model (HSTAFM) is proposed for producing daily synthetic fine-resolution fusions. The suggested model uses only one prior or posterior image pair, especially with the aim being to predict arbitrary temporal changes. The proposed model is implemented in two stages. First, the coarse-resolution image is enhanced through super-resolution based on sparse representation; second, a pre-selection of temporal change is performed. It then adopts a two-level strategy to select similar pixels, and blends multi-sensor features adaptively to generate the final synthetic data. The results of tests using both simulated and actual observed data show that the model can accurately capture both seasonal phenology change and land-cover-type change. Comparisons between HSTAFM and other developed models also demonstrate our proposed model produces consistently lower biases.
出版社接受日期08.09.2016
著者CHEN Bin, HUANG Bo, XU Bing
期刊名稱International Journal of Digital Earth
出版年份2017
卷號10
期次6
頁次639 - 655
國際標準期刊號1753-8947
語言美式英語
關鍵詞Sparse representation, conversion coefficients, pre-selection of temporal change, spatiotemporal fusion

上次更新時間 2020-07-08 於 00:17