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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (6) : 118-127     DOI: 10.6046/zrzyyg.2024327
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Deep forest-based model for detecting changes in remote sensing images
GE Lihua1,2(), WANG Peng3,4(), ZHANG Yanqin1, ZHAO Shuanglin5
1. College of Electronics and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2. Hebei Key Laboratory of Resource and Environmental Disaster Mechanism and Risk Monitoring, Langfang 065201, China
3. Key Laboratory of Airborne Geophysics and Remote Sensing Geology, Ministry of Natural Resources, Beijing 100083, China
4. Shenzhen Research Institute, Nanjing University of Aeronautics and Astronautics, Shenzhen 518110, China
5. Hunan Institute of Geological Disaster Investigation and Monitoring, Changsha 416099, China
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Abstract  

The deep learning-based models currently available for detecting changes in remote sensing images face several challenges, including limited multi-granularity, poor classification performance of networks, high sensitivity to parameters, and great efforts in parameter adjustment. To address these challenges, this study proposed a deep forest-based model for detecting changes in remote sensing images. Initially, preliminary results were determined using a basic change detection method. Then, the results were optimized using the multi-granularity scanning characteristics and strong data classification of deep forest sub-networks. In this manner, the final change detection results were obtained. Verification experiments conducted on the LEVIR-CD and SYSU-CD datasets using various common change detection models indicated that the proposed deep forest-based model significantly outperformed other models in terms of precision, F1 score, and recall. Additionally, the proposed model exhibited strong adaptability on small datasets, as verified by loss function comparison, small-sample experiments, and ablation studies. This adaptability can reduce the complexity of parameter adjustment and address the issues that other deep learning sub-networks fail to be applicable to medium and small datasets.
Keywords remote sensing      change detection      deep forest      multi-granularity scanning      cascade forest     
ZTFLH:  TP79  
  TN912.3  
Issue Date: 31 December 2025
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Lihua GE
Peng WANG
Yanqin ZHANG
Shuanglin ZHAO
Cite this article:   
Lihua GE,Peng WANG,Yanqin ZHANG, et al. Deep forest-based model for detecting changes in remote sensing images[J]. Remote Sensing for Natural Resources, 2025, 37(6): 118-127.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024327     OR     https://www.gtzyyg.com/EN/Y2025/V37/I6/118
Fig.1  Overall flow chart of CDMDF
Fig.2  A schematic representation of the multi-grained scanning
Fig.3  A schematic representation of the cascade forest
Fig.4  Example of LEVIR-CD dataset
Fig.5  Example of SYSU-CD dataset
Fig.6  Results of the different methods on the LEVIR-CD test sets
Fig.7  Results of the different methods on the SYSU-CD test sets
方法 LEVIR-CD SYSU-CD
准确率 召回率 F1 准确率 召回率 F1
FC-EF 0.922 0.773 0.841 0.721 0.902 0.801
FC-EF_DF 0.942 0.966 0.954 0.804 0.827 0.815
FC-Siam-conc 0.910 0.850 0.879 0.695 0.831 0.757
FC-Siam-conc_DF 0.927 0.935 0.930 0.783 0.853 0.817
FC-Siam-diff 0.914 0.861 0.887 0.991 0.632 0.772
FC-Siam-diff_DF 0.931 0.952 0.941 0.988 0.805 0.887
SNUnet 0.864 0.947 0.904 0.689 0.718 0.703
SNUnet_DF 0.939 0.933 0.936 0.855 0.926 0.889
DTCDSCN 0.883 0.904 0.893 0.810 0.857 0.833
DTCDSCN_DF 0.918 0.887 0.902 0.807 0.869 0.837
STANet 0.918 0.833 0.873 0.792 0.838 0.814
STANet_DF 0.924 0.867 0.894 0.903 0.806 0.852
Tab.1  Comparison of the two test sets
损失函数 准确率 召回率 F1
交叉熵损失 0.942 0.966 0.954
均方误差损失 0.902 0.979 0.939
平方损失 0.948 0.920 0.934
焦点损失 0.946 0.951 0.948
Tab.2  The effect of different loss functions
Fig.8  Results of small sample datasets on the LEVIR-CD
方法 原始数据集 小数据集
准确率 召回率 F1 准确率 召回率 F1
FC-EF 0.922 0.773 0.841 0.817 0.742 0.778
FC-EF_DF 0.942 0.966 0.954 0.920 0.931 0.925
FC-Siam-conc 0.910 0.850 0.879 0.883 0.752 0.812
FC-Siam-conc_DF 0.927 0.935 0.930 0.920 0.844 0.880
FC-Siam-diff 0.914 0.861 0.887 0.835 0.786 0.810
FC-Siam-diff_DF 0.931 0.952 0.941 0.897 0.904 0.900
SNUnet 0.864 0.947 0.904 0.833 0.898 0.864
SNUnet_DF 0.939 0.933 0.936 0.879 0.903 0.891
DTCDSCN 0.883 0.904 0.893 0.796 0.847 0.821
DTCDSCN_DF 0.918 0.887 0.902 0.880 0.862 0.871
STANet 0.918 0.833 0.873 0.801 0.799 0.799
STANet_DF 0.924 0.867 0.894 0.877 0.830 0.853
Tab.3  The effect of the datasets size
Fig.9  Comparison of 2 post-processing methods
方法 平均运行时间 增加的时间
FC-EF 10.08 2.12
FC-EF_DF 12.20
FC-Siam-conc 10.53 2.28
FC-Siam-conc_DF 12.81
FC-Siam-diff 9.41 2.06
FC-Siam-diff_DF 11.47
SNUnet 10.49 2.35
SNUnet_DF 12.84
DTCDSCN 16.19 2.26
DTCDSCN_DF 18.45
Tab.4  The effect of the deep forest module on running time (ms/张)
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