## 2 Related Work
### Change Detection Based on Deep Convolutional Neural Networks  
A CNN is a deep learning architecture and has been successfully employed for image analysis tasks. Deep learning approaches can outperform shallow machine learning approaches in terms of accuracy if an adequate amount of training data is supplied [25]. Convolutional operations are employed to produce feature maps (\(F\)) and subsampling layers, followed by fully connected (FC) layers to produce class probability outputs (\(P_{class}\)). Several CNN architectures have been proposed for change detection. [PERSON] et al. proposed a dual-dense CNN for change detection [13]. They designed two-path networks based on the Siamese network with a dense connection for the pair patch image. In the last layer, the Euclidean distance is used to measure the dissimilarity between two patch images, which represents the change classification corresponding to the patch center pixel. They improved it by combining dual- and single-path networks [14]. They also introduced post-processing to enhance the detection rate of change detection. [PERSON] et al. [15] proposed a spectral-spatial joint from a deep network that is similar to the Siamese CNN (SCNN). In this case, the last layer of the SCNN joins two feature maps and feeds them into an FC layer for pixel change classification. In addition, [PERSON] et al. [16] applied the SCNN to change detection by computing the distance between two patch images.  
All the algorithms mentioned above are based on patch-based change detection such that a patch around a center pixel is used to determine whether the center pixel is changed or unchanged. This approach incurs a high computational burden because it must identify all the pixels using CNN-based prediction. Image-based change detection with CNN is more effective than patch-based methods, which generate a change map based on image segmentation. Hence, in this study, image-based change detection using semantic image segmentation based on a CNN was implemented.  
Semantic image segmentation labels each pixel of an input image with the corresponding class that is being represented. Many image segmentation algorithms based on CNNs have been proposed, such as deconvolution semantic segmentation (DeconvNet) [26] and U-Net [23]. U-Net is precise and fast for segmentation and outperforms the previous best method. It combines the local information by observing contextual information in the encoder and extracting general information combining localization and context in the decoder. The encoder path comprises four blocks. Each block involves 3 \(\times\) 3 convolution layers, activation layers (ReLU), batch normalization, and 2 \(\times\) 2 max pooling, starting with 64 feature maps or channels for the first block, and 128 for the second block. In addition, the decoder path consists of 2 \(\times\) 2 unpooling, 3 \(\times\) 3 convolution layers, ReLU function, and batch normalization. To enhance feature representation, there is a skip connection in each layer to connect the feature maps from the encoder path to the decoder path via concatenation.  
For change detection, U-Net was used by [PERSON] et al. to generate a change map [21]. They employed dual layers to receive each image input and generate adaptive difference images via absolute image subtraction. Then, U-Net segments the DI features, producing a change map. In addition, U-Net++ was also proposed by [PERSON] et al. for change detection [22]. They used the backbone of U-Net but varied depths whose decoders were densely connected at the same resolution via the redesigned skip pathways. They also used JF from two images as input. Recently, U-Net was improved by introducing an attention gate to find the attention of the target. The improved U-Net, denoted attention U-Net [24], improves the model sensitivity and accuracy of foreground pixels without incurring significant computation overhead.