The Improved Changeformer for Remote Sensing Change Detection

With advancements in remote sensing satellite technology, change detection (CD) has become a crucial technique in remote sensing image processing. Traditional CD methods, including algebra-based, transformation-based, and classification-based approaches, have contributed significantly to change analysis but face challenges such as misclassification and limited adaptability. Recent developments in deep learning, particularly convolutional neural networks (CNNs) and fully convolutional networks (FCNs), have improved CD accuracy by enabling pixel-level predictions. However, CNN-based methods struggle with multi-scale feature extraction and distinguishing between change and static information. To address these limitations, attention mechanisms and Transformer-based architectures, such as ChangeFormer, have been introduced to enhance long-range dependency modeling and spatial feature representation. Additionally, the Atrous Spatial Pyramid Pooling (ASPP) module further improves multi-scale feature extraction by expanding the network’s receptive field without reducing resolution. This study proposes integrating ASPP into deep learning models to enhance the efficiency, accuracy, and robustness of change detection in complex remote sensing applications.