Integration of SAR polarimetric parameters and multi-spectral data for object-based land cover classification

doi:10.11947/j.AGCS.2019.20170746

Abstract

Abstract: An object-based approach is proposed for land cover classification using optimal polarimetric parameters. The ability to identify targets is effectively enhanced by the integration of SAR and optical images. The innovation of presented method can be summarized in the following two main points:① estimating polarimetric parameters (H-A-α decomposition) through optical image as a driver; ② a multi-resolution segmentation based on optical image only is deployed to refine classification results. The proposed method is verified by using Sentinel-1/2 datasets over Bakersfield area, California.The results are compared against those from pixel-based SVM classification using the ground truth from the National Land Cover Database (NLCD). A detailed accuracy assessment complied for seven classes of surfaces shows that the proposed method outperforms the conventional approach by around 10%, with an overall accuracy of 92.6% over regions with rich texture.

Key words: synthetic aperture radar(SAR), polarimetric, multispectral, data fusion, object-based, land-cover classification

CLC Number:

P237

ZHAO Yi, JIANG Mi. Integration of SAR polarimetric parameters and multi-spectral data for object-based land cover classification[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(5): 609-617.

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