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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (5) : 73-90     DOI: 10.6046/zrzyyg.2024206
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Self-learning segmentation of high-resolution remote sensing images based on visual dual-drive cognition
WU Zhijun1(), CONG Ming1(), XU Miaozhong2, HAN Ling1, CUI Jianjun1, ZHAO Chaoying1, XI Jiangbo1, YANG Chengsheng1, DING Mingtao1, REN Chaofeng1, GU Junkai1, PENG Xiaodong1, TAO Yiting2
1. College of Geological Engineering and Geomatics，Chang’an University，Xi’an 710064，China
2. State Key Laboratory of Information Engineering in Surveying，Mapping and Remote Sensing，Wuhan University，Wuhan 430072，China
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Abstract  

The current high-resolution remote sensing images involve complex scenes that are difficult to analyze. Meanwhile，owing to the diverse scenes，there is a lack of accurate reference obtained from the sample database. Therefore，this paper proposed a self-learning segmentation method for high-resolution remote sensing images，with reference to the visual dual-drive cognition mechanism. Based on the principle of visual perception，this method interpreted the typical ground objects in the scene through unsupervised adaptive analysis. In addition，it achieved self-learning identification of typical ground objects by integrating a neural network. Finally，the segmentation results were self-checked and corrected by combining unsupervised analysis and neural network learning. Using real high-resolution remote sensing image data containing complex ground scenes，the comparative experiments were conducted between the proposed method and two popular deep neural network segmentation methods：mask region-based convolutional neural network （Mask R-CNN） and scalable vision transformer （ScalableViT）. The results showed that the proposed method can maintain robust and reliable segmentation accuracy，and outperformed others in terms of ground object cognition，generalization performance，and anti-interference ability. As such，it proved to be a cost-effective and practical approach.
Keywords bionic visual      high-resolution remote sensing      image segmentation      unsupervised analysis      deep learning neural network      self-learning method     
ZTFLH:  TP753  
  P237  
Issue Date: 28 October 2025
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Zhijun WU
Ming CONG
Miaozhong XU
Ling HAN
Jianjun CUI
Chaoying ZHAO
Jiangbo XI
Chengsheng YANG
Mingtao DING
Chaofeng REN
Junkai GU
Xiaodong PENG
Yiting TAO
Cite this article:   
Zhijun WU,Ming CONG,Miaozhong XU, et al. Self-learning segmentation of high-resolution remote sensing images based on visual dual-drive cognition[J]. Remote Sensing for Natural Resources, 2025, 37(5): 73-90.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024206     OR     https://www.gtzyyg.com/EN/Y2025/V37/I5/73
Fig.1  Adaptive segmentation method for simulating visual dual drive cognition
Fig.2  Multi-scale representation example map of independent ground objects
Fig.3  Superpixel clustering forms an example map of homologous reliable samples
Fig.4  Feature mining structure
Fig.5  Self-attention weight allocation structure
Fig.6  Multi-scale differential coding structure
Fig.7  Multiple receptive field decoding structure
Fig.8  Global network structure
Fig.9  Error repair ideas
Fig.10  Adaptive segmentation process simulating visual dual drive cognition
Fig.11  Segmentation results of experiment 1
精度
指标		 MR SViT 本文方法
整张影像 小区域1 小区域2 小区域3 整张影像 小区域1 小区域2 小区域3 整张影像 小区域1 小区域2 小区域3
OA 0.861 0.858 0.867 0.950 0.961 0.966 0.882 0.883 0.890 0.715 0.648 0.728
K 0.742 0.734 0.752 0.484 0.508 0.543 0.669 0.664 0.675 0.542 0.445 0.556
QD 0.052 0.048 0.053 0.026 0.017 0.016 0.064 0.058 0.051 0.172 0.098 0.172
AD 0.087 0.094 0.079 0.024 0.022 0.018 0.054 0.059 0.059 0.113 0.254 0.100
Tab.1  Segmentation accuracy of experiment 1
精度
指标		 MR SViT 本文方法
整张影像 小区域1 小区域2 小区域3 整张影像 小区域1 小区域2 小区域3 整张影像 小区域1 小区域2 小区域3
OA 0.799 0.918 0.642 0.741 0.767 0.896 0.616 0.652 0.815 0.922 0.645 0.761
K 0.690 0.574 0.513 0.649 0.639 0.522 0.485 0.535 0.678 0.587 0.523 0.675
QD 0.086 0.040 0.252 0.156 0.084 0.060 0.271 0.191 0.07 0.059 0.250 0.090
AD 0.119 0.032 0.106 0.102 0.149 0.043 0.113 0.157 0.111 0.029 0.105 0.149
Tab.2  Segmentation accuracy of experiment 2
Fig.12  Segmentation results of experiment 2
Fig.13  Segmentation results of experiment 3
精度
指标		 MR SViT 本文方法
整张影像 小区域1 小区域2 小区域3 整张影像 小区域1 小区域2 小区域3 整张影像 小区域1 小区域2 小区域3
OA 0.861 0.939 0.819 0.852 0.856 0.934 0.829 0.824 0.876 0.951 0.850 0.893
K 0.787 0.902 0.669 0.716 0.782 0.896 0.691 0.685 0.810 0.920 0.727 0.799
QD 0.053 0.054 0.109 0.133 0.058 0.054 0.117 0.161 0.056 0.027 0.088 0.088
AD 0.086 0.007 0.072 0.015 0.086 0.012 0.054 0.015 0.068 0.022 0.062 0.019
Tab.3  Segmentation accuracy of experiment 3
精度
指标		 MR SViT 本文方法
整张影像 小区域1 小区域2 小区域3 整张影像 小区域1 小区域2 小区域3 整张影像 小区域1 小区域2 小区域3
OA 0.774 0.709 0.863 0.779 0.774 0.738 0.787 0.781 0.803 0.765 0.851 0.775
K 0.705 0.522 0.796 0.617 0.697 0.538 0.686 0.607 0.735 0.568 0.779 0.599
QD 0.135 0.241 0.05 0.133 0.159 0.216 0.160 0.156 0.110 0.157 0.082 0.149
AD 0.091 0.050 0.087 0.088 0.067 0.046 0.053 0.063 0.087 0.078 0.067 0.076
Tab.4  Segmentation accuracy of experiment 4
Fig.14  Segmentation results of experiment 4
精度
指标		 MR SViT 本文方法
整张影像 小区域1 小区域2 小区域3 整张影像 小区域1 小区域2 小区域3 整张影像 小区域1 小区域2 小区域3
OA 0.800 0.670 0.686 0.521 0.792 0.644 0.750 0.515 0.816 0.677 0.758 0.533
K 0.713 0.528 0.548 0.299 0.703 0.502 0.629 0.309 0.719 0.530 0.631 0.301
QD 0.108 0.273 0.162 0.385 0.131 0.304 0.082 0.405 0.113 0.264 0.082 0.319
AD 0.092 0.057 0.152 0.094 0.077 0.052 0.168 0.080 0.070 0.039 0.151 0.078
Tab.5  The segmentation accuracy of experiment 5
Fig.15  Segmentation results of experiment 5
精度
指标		 MR SViT 本文方法
整张影像 小区域1 小区域2 小区域3 整张影像 小区域1 小区域2 小区域3 整张影像 小区域1 小区域2 小区域3
OA 0.822 0.788 0.825 0.904 0.854 0.820 0.861 0.909 0.855 0.821 0.863 0.919
K 0.623 0.649 0.682 0.655 0.665 0.686 0.736 0.617 0.673 0.694 0.736 0.647
QD 0.065 0.103 0.065 0.045 0.074 0.077 0.040 0.050 0.072 0.075 0.052 0.034
AD 0.113 0.109 0.110 0.051 0.072 0.103 0.097 0.041 0.071 0.104 0.087 0.047
Tab.6  The segmentation accuracy of experiment 6
Fig.16  Segmentation results of experiment 6
Fig.17  Comparison of the accuracy of the three methods
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