Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features

Abstract

Land Use and Land Cover (LULC) classification is vital for environmental and ecological applications. Sentinel-2 is a new generation land monitoring satellite with the advantages of novel spectral capabilities, wide coverage and fine spatial and temporal resolutions. The effects of different spatial resolution unification schemes and methods on LULC classification have been scarcely investigated for Sentinel-2. This paper bridged this gap by comparing the differences between upscaling and downscaling as well as different downscaling algorithms from the point of view of LULC classification accuracy. The studied downscaling algorithms include nearest neighbor resampling and five popular pansharpening methods, namely, Gram-Schmidt (GS), nearest neighbor diffusion (NNDiffusion), PANSHARP algorithm proposed by Y. Zhang, wavelet transformation fusion (WTF) and high-pass filter fusion (HPF). Two spatial features, textural metrics derived from Grey-Level-Co-occurrence Matrix (GLCM) and extended attribute profiles (EAPs), are investigated to make up for the shortcoming of pixel-based spectral classification. Random forest (RF) is adopted as the classifier. The experiment was conducted in Xitiaoxi watershed, China. The results demonstrated that downscaling obviously outperforms upscaling in terms of classification accuracy. For downscaling, image sharpening has no obvious advantages than spatial interpolation. Different image sharpening algorithms have distinct effects. Two multiresolution analysis (MRA)-based methods, i.e., WTF and HFP, achieve the best performance. GS achieved a similar accuracy with NNDiffusion and PANSHARP. Compared to image sharpening, the introduction of spatial features, both GLCM and EAPs can greatly improve the classification accuracy for Sentinel-2 imagery. Their effects on overall accuracy are similar but differ significantly to specific classes. In general, using the spectral bands downscaled by nearest neighbor interpolation can meet the requirements of regional LULC applications, and the GLCM and EAPs spatial features can be used to obtain more precise classification maps.

Multitemporal 2016-2018 Sentinel-2 Data Enhancement for Landscape Archaeology: The Case Study of the Foggia Province, Southern Italy

“…The resampling function is a native function implemented in SNAP and allows pixel resolution re-scaling to benefit from all Sentinel-2 bands. Bands 2, 3, 4, and 8 (blue, green, red and NIR) were re-sampled at a 10 m spatial resolution [64][65][66].…”

Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa

“…We retrieved three S-2 data from their open access hub, and the images were corrected atmospherically, using the Sentinel Applications Platform (SNAP) version 7.0 with the help of Sen2Cor 4, an atmospheric correction toolbox. We resampled bands with different spatial resolutions to 10 m using the nearest neighbor interpolation method [45]. This was employed to ensure that the pixel size of S-2 images corresponded with the identified maize field.…”

Comparing Sentinel-2 MSI and Landsat 8 OLI Imagery for Monitoring Selective Logging in the Brazilian Amazon

“…The SWIR-2 band in Sentinel-2 imagery comes at a 20 m resolution, therefore it was resampled to 10 m resolution using the nearest neighbor resampling method. For Sentinel-2 imagery, considering land use and land cover maps, downscaling has been shown to have superior performance compared to upscaling, even when based on the most straightforward technique, the nearest neighbor resampling [59]. For this reason and also because we aimed at small-scale disturbances, downscaling was preferred over upscaling.…”