# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 3 Results
### Comparison and Analysis
#### 3.2.3 Ablation Studies--Testing on the PIESAT-CD Dataset  
To verify each decoding component's contribution to STDecoder-CD, we designed three ablation experiments on the PIESAT-CD dataset as follows:  
The Type I decoder will become unavailable if we ablate any components from it, so in order to verify each component's significance, we chose to adopt the replacement strategy instead of the ablation strategy. Here, we replaced Difference Detector I, as shown in Figure 2, with Difference Detector II, as shown in Figures 3 and 4. Likewise, there seem to be no independent and separable components in the Type II decoding architecture. As shown in Figure 8b, here, we replaced the right half of the FPN module with the Type I decoder so as to verify which of the two fusion methods, i.e., one that takes the \"many-to-many\" strategy to fuse the multi-scale deep features and one that takes the \"many-to-one\" feature fusion strategy, is much more efficient in enhancing the CD performance. Note that the Type II decoder introduces deep supervision after each feature fusion operation, but in order to keep the workflow consistent with the FPN's (see Figure 8a) and make a fair comparison, here, we introduce deep supervision only after the Y1 de block. For the Type III decoder, an actual ablation experiment can be conducted by removing the transformer decoding component in Figure 4a. The parameters of the three networks are the same during the training process, and the relevant experimental results are summarized in Table 3 below, from which we can derive the following observations:  
1. For the Type I decoder, the CD performance of Difference Detector II is only slightly better than that of Difference Detector I in terms of recall, F1 score, and IOU, but the parameters of the former are 10.95M higher than the latter. This implies that the advantage of Difference Detector II is not obvious. [PERSON] et al. [30] further pointed out that the CD accuracy derived from Difference Detector I is several percentage points higher than that derived from the SC or SD detector. Herein, the SC detector only uses Siamese concatenation operations to fuse the hierarchical deep features, and SD only uses Siamese subtraction operations for fusion. Thus, jointly considering the efficiency, accuracy, and robustness, Difference Detector I is still the optimal option for the Type I decoder. 2. For the Type II decoder, the CD performance using the \"many-to-one\" strategy is slightly better than the \"many-to-many\" strategy in terms of precision, recall, F1, and IOU. One possible reason for this observation is that: the \"many-to-one\" strategy involves more Conv3 ReLU operations, so it can enrich the representation of feature information. In addition, the \"many-to-one\" strategy can preserve the feature diversity by integrating the hierarchical deep features into a singular feature vector using a parallel architecture, whereas the \"many-to-many\" strategy processes the hierarchical features in series. Shifting from parallel to series might result in loss of information and the destruction of feature diversity. Moreover, the absence of deep supervision on Y\({}^{2}\) and Y\({}^{3}\) in Figure 8b may also hurt the accuracy of the \"many-to-many\" strategy. 3. In the BIT model [19], the transformer decoder (MHCA) was successfully used as a decoder to enhance the original features with context-rich tokens by modeling the long-range dependencies. However, as displayed in Table 3, the CD performance of Type III without MHCA is slightly better than the original Type III with it, although the difference is not large. This implies that the transformer decoder is not essential to Type III STDecoder-CD. One possible reason for this observation is that: the deep features generated by TransCNN itself are context-rich, and there may be no need to guide the network to model the long-range relations again using MHCA.