Remote sensing feature identification of river channel by fusing multimodal data

doi:10.13474/j.cnki.11-2246.2025.1231

Abstract

Abstract: Aiming at the lack of feature classification accuracy of river features in UAV remote sensing due to spectral confusion and shadow masking,this study proposes a semantic segmentation method based on DeepLabV3+ for RGB-DSM multimodal data fusion.The method integrates the spectral information of RGB images from UAVs and the DSM elevation information generated from laser point clouds,and introduces the “spectral-structural” feature synergy mechanism to realize the pixel-level accurate recognition of typical river features,such as water bodies,embankment slopes,trees,and so on.The experimental results show that,compared with RGB,the pixel accuracy (PA)of the model is increased from 93.47% to 95.06%,and the mean intersection ratio (mIoU)is increased from 81.60% to 84.38%,with a significant increase in the IoU of the tree category under the RGB-DSM multimodal data input.The visual comparison shows that the inclusion of DSM elevation information effectively improves the problems of road boundary blurring and tree shadow misclassification,and significantly enhances the spatial structure representation of complex features.This paper demonstrates that RGB-DSM multimodal fusion has unique advantages in improving the semantic segmentation accuracy of river features.

Key words: UAV remote sensing, river feature recognition, semantic segmentation, DeepLabV3+, RGB-DSM

CLC Number:

P237

WANG Chao, FU Qiang, CUI Zhifang, TANG Tian. Remote sensing feature identification of river channel by fusing multimodal data[J]. Bulletin of Surveying and Mapping, 2025, 0(12): 178-183.

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