ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection

arXiv:2403.17909v1 Announce Type: new
Abstract: Deep learning has shown remarkable success in remote sensing change detection (CD), aiming to identify semantic change regions between co-registered satellite image pairs acquired at distinct time stamps. However, existing convolutional neural network and transformer-based frameworks often struggle to accurately segment semantic change regions. Moreover, transformers-based methods with standard self-attention suffer from quadratic computational complexity with respect to the image resolution, making them less practical for CD tasks with limited training data. To address these …

aggregation arxiv change context cs.cv detection global sensing type