Extracting urban impervious area from multi-source image fusion data assisted by global land cover data

doi:10.13474/j.cnki.11-2246.2024.0204

Abstract

Abstract: In this paper, an automatic extraction method of urban impervious surface area from multi-source image fusion data is proposed using GlobeLand30 data asauxiliary data. Firstly, an image fusion method based on band mapping and wavelet transform is proposed to fuse Sentinel-2 and GF-2 images to obtain fusion images with high spatial resolution and spectral resolution. The fusion image has rich spectral and spatial characteristics, which is conducive to improving the ability of distinguishing impervious and non-impervious surface in complex urban areas. Then, the category information of GlobeLand30 data is used to automatically to obtain the initial samples, a variety of ground index such as vegetation index, water index, and built-up area index are constructed to refine the initial samples. Finally, the optimized training samples are used to train the classifier with spectral and ground index features to achieve automatic and accurate extraction of urban impervious surface. In this paper, images of GF-2 and Sentinel-2 in Jinan city in 2019 are used as experimental data. With the help of GlobeLand30 global land cover data with different phases, resolutions and images, the overall accuracy of impervious surface extraction is better than 92%, which verifies the effectiveness of the proposed method.

Key words: GlobeLand30 land cover data, GF-2 satellite, Sentinel-2 satellite, image fusion, impervious surface extraction

CLC Number:

P237

HUO Jiating, ZHAO Zhan, ZHU Xiuli. Extracting urban impervious area from multi-source image fusion data assisted by global land cover data[J]. Bulletin of Surveying and Mapping, 2024, 0(2): 19-25.

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