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.
Highlights
Land Use and Land Cover (LULC) maps describe the composition and distribution of the natural elements on the land surface and reflect the anthropogenic effects on these elements [1]
The multiresolution analysis (MRA)-based pansharpening algorithms can highly preserve the spectral characteristics since its spatial information obtained from the high resolution (HR) band and spectral information obtained from the low resolution multispectral (LRMS) image
The textural metrics of Grey-Level-Co-occurrence Matrix (GLCM) and morphological profiles of extended attribute profiles (EAPs) were integrated with spectral bands in a vector composite way to take full advantages of image sharpening [35]
Summary
Land Use and Land Cover (LULC) maps describe the composition and distribution of the natural elements on the land surface and reflect the anthropogenic effects on these elements [1]. Pansharpening can be defined as a pixel-level fusion, which is to merge the geometric details of a high resolution (HR) image into low resolution multispectral (LRMS) bands [21,22] It increases the spatial information at the cost of a certain degree of spectral information loss, represented by a more or less color distortion [23]. As the sensor and application dependence of fusion quality, the effects of different image sharpening algorithms on LULC classification have been investigated using MODIS and ASTER [27,28]. The specific research objectives are to: (1) evaluate the effects of the two different spatial resolution unification schemes, i.e., upscaling and downscaling, on classification accuracy of Sentinel-2 imagery; (2) assess the role of image sharpening techniques in LULC classification; and (3) investigate the value of spatial features, i.e., EAPs and GLCM, in LULC classification.
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