# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 3 Results
### Comparison and Analysis  
#### 3.2.1 The Performance of the Proposed Models  
In this section, we compare the CD performance of our STDecoder-CD (Types I, II, and III) with several existing SOTA (state-of-the-art) methods:  
1. FC-Siam-Diff [6] is a feature-difference method, which extracts multi-level features of bitemporal images from a Siamese UNet architecture, and their difference is used to detect changes. 2. STANet [5] is a Siamese-based spatial-temporal attention network for CD. Note that STANet is fine-tuned on the ImageNet-pretrained ResNet-18 model. 3. IFN [12] is a multi-scaled feature concatenation method. It fuses multi-level deep representations of bitemporal images with image difference representations by means of attention modules for change map reconstruction. It uses pretrained VGG16 as the backbone network. 4. ChangeSTAR [17] is a multi-task architecture for joint semantic segmentation and object change detection. It can reuse any deep semantic segmentation architecture via the elaborately designed ChangeMixin module. ChangeSTAR uses a pretrained deep network (ResNet101) as the encoder. In this analysis, we set the number of iterations to 5600, and the batch size was set to 4 for input images with a size of 512 \(\times\) 512 and to 16 for images with a size of 256 \(\times\) 256. The SGD optimizer with momentum = 0.9 and weight_decay = 0.0001 was used during the training process. 5. SNUNet-CD [13] uses a densely connected (NestedUNet) Siamese network for change detection. 6. SiUNet3+-CD [1] is a Siamese UNet3+-based CD network architecture. Note that SiUNet3+-CD uses the GN (Group Normalization) layer to normalize the inputs to a hidden layer so that the batch size can be set as small as possible [5] in conditions of insufficient computational resources. At the suggestion of [1], the maximum epoch of iterations was set to 70, and the original learning rate was \(1\times 10^{-3}\). 7. BIT [19] is a transformer-based CD method. It uses a transformer encoder-decoder network to enhance the context information of CNN features via semantic tokens followed by feature differencing to obtain the change map. 8. ChangeFormer [20] is a transformer-based Siamese network for CD. It uses a hierarchical transformer encoder in a Siamese architecture with a simple MLP decoder. We downloaded the pretrained model of ChangeFormer from [[http://www.dropbox.com/s/undtrixiz/bkg5/pretrained_changeformer.pt?dl=0](http://www.dropbox.com/s/undtrixiz/bkg5/pretrained_changeformer.pt?dl=0)]([http://www.dropbox.com/s/undtrixiz/bkg5/pretrained_changeformer.pt?dl=0](http://www.dropbox.com/s/undtrixiz/bkg5/pretrained_changeformer.pt?dl=0)) (accessed on 20 February 2022). To enable a fair comparison, whenever possible, we used the reported performance from the corresponding publications. Otherwise, if not specified, we re-implemented the algorithms based on public codes or using our own Python codes with parameter settings consistent with the relevant references. The same online data augmentation strategy (as mentioned in Section 3.1 was applied to all of the compared methods. There are several important observations from Table 1:  
On the CDD dataset, the BIT model with the pretrained ResNet as its backbone achieves the best CD performance in terms of F1 score, IOU, precision, and recall metrics.