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Shadow Detection of Integrated Characteristic Components for GF-2 Image |
Qiang Li(),Dejun Feng(),Minjun Hu,Yiyao Wu,Lihui Yang |
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611730, China |
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Abstract The shadow detection accuracy in the high-resolution remote sensing images is easily disturbed by water, vegetation and so on. This study proposed a shadow detection method based on object-oriented method and established characteristic components by analyzing the spectral characteristics of typical features in GF-2 satellite images.The following components were constructed to detect shadow information: first principal component (PC1), brightness component I, Normalized Difference Vegetation Index (NDVI) and Water Index (WI). And then, we normalized each characteristic component to establish a rule set containing features such as band mean, standard deviation. Brightness I and PC1 were chosen as the main data source for multi-resolution segmentation, at last, performed object-oriented method on the segmented images to detect shadow. Selected different areas of GF-2 images for the proposed method, and experimental results show that the proposed method could extract complete shadow patches and effectively reduce the influence of water bodies and vegetation compared with pixel-based method.
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Received: 21 August 2018
Published: 23 March 2020
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Corresponding Authors:
Dejun Feng
E-mail: qiangli8898@gmail.com;fengdj@126.com
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