Semisupervised Remote Sensing Image Building Change Detection Algorithm

Change detection in buildings holds significant importance in the fields of remote sensing image processing and pattern recognition. However, data annotation has always been a prominent challenge in the application of deep learning algorithms, especially in change detection scenarios. To address the data annotation challenges in deep learning-based change detection algorithms, this paper proposes an innovative semi-supervised learning method. This method employs a Siamese network that fuses bi-temporal features for feature extraction and constructs a teacher-student network framework for semi-supervised model training. To further enhance the accuracy of semi-supervised change detection, this paper introduces random perturbations in deep features to achieve consistency regularization. Additionally, on the level of image deep features, the paper proposes a method for forming decision boundaries by capturing differences in bi-temporal image features to distinguish changes in bi-temporal images. This method achieved Intersection over Union (IoU) scores of 83.04% and 85.57% on the Levir-CD and WHU Building datasets, respectively. Experimental results show that the proposed method can achieve performance levels close to fully supervised training with a limited amount of labeled data.