BCDetNet: a deep learning architecture for building change detection from bi-temporal high resolution satellite images

Abstract

Change detection is becoming more and more popular technology for the analysis of remote sensing data and is very important for an accurate understanding of changes that are happening in the Earth’s surface. Different Deep Learning methods proposed till now are mainly focused on simple networks which results in poor detection for small changed areas because they can not differentiate between the bi-temporal image’s characteristics. To solve this problem, this article proposes a novel Building Change Detection Network (BCDetNet) for building object change detection and its analysis from bi-temporal high resolution satellite image. The proposed BCDetNet model can detect small change areas with the help of multiple feature extraction block. The proposed BCDetNet model executes building change detection using bi-temporal high resolution satellite images. The proposed BCDetNet model is trained on two publicly available datasets namely LEVIR and WHU change detection(CD) datasets. These datasets contain RGB images with dimensions of (1024 × 1024) and (512 × 512), respectively. The BCDetNet model can learn from scratch during training and performs better than the benchmark change detection models with fewer trainable parameters. The BCDetNet model gives Recall—94.06%, Precision—93.00%, Jaccard score—88.40%, Accuracy—98.73%, F1 score—93.52% and Kappa coefficient—87.05% on LEVIR CD dataset and Recall—89.51%, Precision —92.78%, Jaccard score - 84.38%, Accuracy—96.78%, F1 score—91.06% and Kappa coefficient - 82.12% on WHU CD dataset. This work is a step in the direction of achieving best results in building change detection from high resolution satellite images. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.