## 4 Results  
### Automatic Extraction Results of New Bare Land  
Based on the Siamese CNN change detection framework proposed in this paper, 20,052 samples were divided into training and validation sets in the ratio of 8:2 for model training.  
Figure 8: Example of bare land change detection samples. (**a**,**d**) Pre-temporal images; (**b**,**e**) Post-temporal image; (**c**,**f**) labels.  
The samples were only enhanced with rotation, mirroring, and dithering. ResNet (Residual Network) was used as the model backbone, and the multi-layer feature merging strategy in the encoding and decoding process is consistent with that in deeplabv3+, which uses the first and last layers for feature merging.  
The accuracy and speed of two different model versions of ResNet, ResNet101, and ResNet152 were comparatively analyzed. After 100 iterations of training, the overall training MIoU was approximately 78% using 6 V100 GPUs (32G memory). The training time of resnet101 and resnet152 was 41 and 55 h, respectively. As their accuracy levels are similar, this study selected resnet101, which has better training and prediction efficiency, as the backbone. Additionally, the hyper-parameters were adjusted to obtain the optimal training model for the prediction of new bare land parcels in the experimental area.  
Based on the trained model, the automatic feature extraction of new bare land was performed in two experimental areas in Yulin and Taiyuan (with an area of approximately 50,000 km\({}^{2}\)). The initial numbers of automatically extracted parcels were 17,733 and 2521 in Yulin and Taiyuan, respectively (Figure 9). Part of the extraction results after the vectorization of parcels are shown in Figure 10. Overall, the model can predict most new bare land types. The objects on pre-temporal images cover a variety of types, such as cultivated land, forest land, grassland, and buildings, it can be seen that the model is relatively robust. However, a large number of missed extractions, false extractions, and inaccurate target morphology have been revealed by manual visual interpretation. The number of automatically extracted parcels and manually identified parcels were counted, and the false detection rate (FDR) and missed detection rate (MDR) of automatically extracted parcels were calculated (Equations (7) and (8)) with a 50% overlap rate criterion. The results are shown in Table 1. The initial FDR of the two experimental areas was relatively high, both above 70%, and the MDR did not exceed 30%.  
The initial results of automatic extraction based on CNN are less than satisfactory for several reasons.  
First, the number of samples was limited, and these samples cannot ensure the complete coverage of the texture, color, shape, and other features of bare land change parcels in the experimental area, which is the main reason for the missed extraction. Second, as shown in Figure 7, for the multi-sensor mosaic images, local areas with cloud cover or large differences in radiation at the borders of the mosaic images may lead to false extraction. Third, the current change detection model based on deep learning mainly adopts supervised learning, which has several limitations. The model is usually strongly dependent on samples. Additionally, for the inconsistency of color, radiation and definition of multi-sensor remote sensing images in practical applications, the robustness of the model needs to be enhanced.  
Figure 10: Example of automatic extracted new bare land parcels.  
Therefore, in this paper, based on the automatically extracted parcels, we performed the optimization processing of the parcel morphology and topology to improve the initial extracted results.