A robust and adaptive spatial-spectral fusion model for PlanetScope and Sentinel-2 imagery. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Zhao, Yongquan; Liu, Desheng
• Is Part Of: GIScience & remote sensing. Volume 59:Issue 1(2022)
• Page Start: 520
• Page End: 546
• Publication Date: 2022-12-31
• Subjects: Spatial-spectral image fusion -- PlanetScope -- sentinel-2 -- very high resolution (VHR) -- medium resolution (med-resolution) -- multispectral; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2022.2036054
• Languages: English
• ISSNs: 1548-1603

Abstract

ABSTRACT: Satellite sensors usually compromise between their spatial and spectral resolutions due to the limitations of data volume and signal-to-noise ratio, such as the Very High spatial Resolution (VHR) PlanetScope (PS, four or five 3-m bands) and medium spatial resolution (med-resolution) Sentinel-2 (S2, ten 10-m or 20-m bands) constellations. Concomitant with the growing demand for satellite images with ample spatial details and spectral signatures, Spatial-Spectral image Fusion (SSF) is important for blending the spatial resolution of PS and the spectral resolution of S2 to produce synthetic 3-m image products in the ten bands for different applications. However, the existing studies conducted for fusing PS and S2 data present limited spatial and spectral fidelities to original PS and S2 bands. Hence, this study presents a new SSF method named Robust and Adaptive Spatial-Spectral image Fusion Model (RASSFM) for that purpose. RASSFM improves the spatial and spectral fidelities of the results through: (1) combining the spectral mapping and the spectral correlation to obtain the high-quality spatial information sources for the S2 bands, (2) utilizing the neighborhood information considering both spatial and spectral constraints to improve the spectral fidelities of the fused bands. We conducted the SSF tests at the degraded and original spatial resolutions to comprehensively evaluate the proposed RASSFM method. Furthermore, we examined the performance of RASSFM in four study sites with highly mixed regular or desert urban, heterogeneous agricultural, and vegetation-dominated landscapes. Moreover, we compared our method with twenty representative SSF methods developed for similar purposes. The results demonstrate that RASSFM not only outperforms the other methods but has robust performance in different landscapes and comparable accuracies in different bands, thereby advancing the fusion of VHR and med-resolution images from PS and S2, respectively.