A spatio-temporal fusion method for remote sensing data Using a linear injection model and local neighbourhood information

Yue Sun, Hua Zhang, Wenzhong Shi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

This paper presents a spatio-temporal fusion method for remote sensing images by using a linear injection model and local neighbourhood information. In this method, the linear injection model is first introduced to generate an initial fused image, the spatial details are extracted from the fine-resolution image at the base date, and are weighted by a proper injection gains. Then, the spatial details and the relative spectral information from the coarse-resolution images are blended to generate the fusion result. To further enhance its robustness to the noise, the local neighbourhood information, derived from the fine-resolution image and the fused result simultaneously, is introduced to refine the initial fused image to obtain a more accurate prediction result. The algorithm can effectively capture phenology change or land-cover-type change with minimum input data. Simulated data and two types of real satellite images with seasonal changes and land-cover-type changes are employed to test the performance of the proposed method. Compared with a spatial and temporal adaptive reflectance fusion model (STARFM) and a flexible spatio-temporal fusion algorithm (FSDAF), results show that the proposed approach improves the accuracy of fused images in phenology change area and effectively captures land-cover-type reflectance changes.

Original languageEnglish
Pages (from-to)2965-2985
Number of pages21
JournalInternational Journal of Remote Sensing
Volume40
Issue number8
DOIs
Publication statusPublished - 18 Apr 2019

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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