Image fusion of Sentinel-2 and CBERS-4 satellites for mapping soil cover in the Wetlands of Pantanal. (3rd April 2017)

• Record Type: Journal Article
• Authors: Pereira, Osvaldo José Ribeiro; Melfi, Adolpho José; Montes, Célia Regina
• Is Part Of: International journal of image and data fusion. Volume 8:Number 2(2017)
• Page Start: 148
• Page End: 172
• Publication Date: 2017-04-03
• Subjects: Image fusion -- image enhancement -- DCK -- ATWT; Image processing -- Periodicals; Multisensor data fusion -- Periodicals; Multisensor data fusion; Periodicals; 621.36705
• DOI: 10.1080/19479832.2016.1261946
• Languages: English
• ISSNs: 1947-9832

Abstract

ABSTRACT: Advances in remote-sensing technology and the release of new sensor systems have been providing a wide range of optical and radar satellite images. The availability of such images gives new options for mapping relatively remote and sparsely settled territories. Given this, the main goal of this research was to perform a quantitative assessment of the quality of a set of fused images obtained by China–Brazil Earth Resources Satellite (CBERS-4) and Sentinel-2 satellites over the region of the Brazilian Pantanal, considering a zonal quantitative approach. Through the applied methods, we combined the spectral resolution of Sentinel-2 images with the spatial resolution of the CBERS-4 panchromatic band. The quantitative evaluation of the spectral and spatial quality of the fused products based on a regionalised approach was essential in order to understand the spatial variability of the fusion quality, according to each fusion method. Based on the obtained results, we observed that CBERS-4 panchromatic band could be satisfactorily applied for refining the spatial resolution of Sentinel-2 multispectral (MS) images according to À trous wavelet transform and downscaling cokriging image fusion methods, allowing the generation of MS fused images with a 5-m spatial resolution. The resulting fused images were used to map the spatial distribution of saline and crystalline lakes at high spatial resolution and with unprecedented spectral and spatial accuracies, according to a semiautomatic classification approach.