Journal of Geo-information Science

MDSNet : A Multi-Scale Depth Supervision Method for High-Resolution Remote Sensing Image Semantic Segmentation

SHAN Huilin, WANG Xingtao, LIU Wenxing, WU Xinyue, GAO Runze, LI Hongxu

Journal of Geo-information Science, 2025, 27(6): 1381-1400. DOI: 10.12082/dqxxkx.2025.250009

Fig. 13 Segmentation of Impervious surfaces and buildings in Potsdam

Other figure/table from this article

Fig. 1 The model structure of MDSNet
Fig. 2 Space De-redundant convolution module
Fig. 3 Feature expressiveness enhances visualization
Fig. 4 The structure of channel reweight concat
Fig. 5 The structure of ResT-Mamba
Fig. 6 The structure of multi-scale convolutional feature fusion module
Fig. 7 Multi-scale convolutional attention module
Tab. 1 Experimental configuration information
Fig. 8 Examples of ISPRS dataset
Fig. 9 Comparison of wavelet transform ablation experiments
Tab. 2 Quantitative results of different branch stems (Vaihingen) (%)
Tab. 3 The effect comparison test of each module (Vaihingen)
Fig. 10 Comparison of CRC ablation experiments
Tab. 4 Segmentation results under different network models on the Vaihingen dataset (%)
Fig. 11 Vaihingen data on the distribution of recall rates
Tab. 5 Segmentation results under different network models on the Potsdam dataset (%)
Fig. 12 Segmentation of low vegetation and trees in Potsdam
Fig. 14 Segmentation of Impervious surfaces and buildings in Potsdam
Fig. 15 Potsdam overall segmentation diagram