## 1 Introduction  
By utilizing multi-temporal remote sensing imageries, dynamic changes on the Earth's surface can be detected automatically. In this process, land cover changes on temporal imageries are identified in the same geographical area. Automatic change detection is widely used for both civil and military applications, such as environmental monitoring, disaster evaluation, urban expansion monitoring, and reconnaissance. In general, the change detection process is composed of three steps: pre-processing, feature extraction, and classification. Pre-processing corrects radiometric and geometric distortions and performs image registration. In the feature extraction stage, the features of land cover changes are extracted for classification by an algorithm into two classes: \"changed\" or \"unchanged.\"  
There are many challenges in identifying land cover changes against the diverse surface structures of Earth and in complex conditions, such as different seasons, different viewing angles, radiometric distortion, and geometric distortion. Many studies have been conducted to develop effective change detection algorithms to identify precise land cover changes while overcoming these problems. Several conventional algorithms based on traditional machine learning have consequently been proposed for change detection [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. The change detection approach based on unsupervised learning with fuzzy C-mean (FCM) clustering [1; 2; 3; 4; 5; 6] is an example of classification of feature vectors of land cover change into classes. In this approach, the feature vectors are extracted from the difference image (DI) using an image subtraction or log-ratio (LR) scheme. Then, they are projected using principal component analysis to reduce the feature dimensions by creating an eigenspace. This approach can be useful when labels are unavailable in the training stage. However,without any additional information, it is difficult to achieve reasonably good performance owing to overfitting for various changes. To address this problem, change detection based on supervised learning is widely applied [7; 8; 9; 10]. In this approach, support vector machine (SVM) learning is employed to classify the feature vectors into binary classes along with given ground truths. This approach extracts linear models from a given dataset to find the optimal hyperplanes for distinguishing the feature vectors. However, handcrafted feature selection and feature extraction methods must be well designed for optimal performance. Recently, deep learning methods, such as deep convolutional neural networks (DCNNs), have been widely employed to learn sophisticated feature spaces from massive amounts of image data. These methods can yield a better performance than traditional machine learning for image analysis tasks. Several deep learning methods have also been proposed to solve change detection problems [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. These DCNNs have evolved to use more parameters and layers to improve performance. In particular, the transformer-based methods have obtained high performance because they use global features [20]. However, the transformer has high computational complexity from using a relatively large number of parameters compared to general DCNNs. U-Net is a DCNN architecture used for image segmentation with a relatively low computational load [21; 22]. It is composed of encoding and decoding path networks to extract the context of features and enable precise localization [23]. U-Net employs a skip connection at each level to bypass features from the encoder to the decoder path, aiming at enhancing the localization capability. A modified U-Net for change detection has also been proposed that first utilizes the adaptive DI features from feature-level subtraction [21]. Then, the U-Net extracts these features to produce a segmented change map. [PERSON] et al. employed Modified-UNet++ for change detection by reallocating skip connections [22].