Affiliation:
1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Hubei Luojia Laboratory, Wuhan University
2. e State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Hubei Luojia Laboratory, Wuhan University
3. National Engineering Research Center of Geographic Information System, China University of Geosciences
Abstract
Shadows in remote sensing images contain crucial information about various features on the ground. In this study, a method for detecting building shadows in GF‐2 images based on improved quick shift was proposed. First, six feature variables were constructed: first principal
component (PC1), brightness component (I), normalized difference shadow index (NDSI), morphological shadow index (MSI), normalized difference water index (NDWI), and normalized difference vegetation index (NDVI). Then, the image was segmented to obtain homogeneous objects, which were then
classified using a random forest model. Two improvements were added to the quick shift algorithm: using PC1, I, and MSI as input data instead of RGB images; and adding Canny edge constraints. Validation in six research areas yields Kappa coefficients of 0.928, 0.896, 0.89, 0.913, 0.879, and
0.909, confirming method feasibility. In addition, comparative experiments demonstrate its effectiveness and robustness across different land cover types while mitigating the segmentation scale effect.
Publisher
American Society for Photogrammetry and Remote Sensing