A Semi-Supervised Semantic and Spatial Change Detail Retention Network for Semantic Change Detection in Remote Sensing Images

The task of semantic change detection (SCD) in remote sensing images (RSIs) is aimed at identifying the multiple “from-to” changes in land cover. However, the prevailing multitask SCD network structure does not directly model the semantic change information, leading to potential inaccuracies in extracting multiple detailed changes. In this article, a semi-supervised semantic and spatial change detail retention network (S3CDRNet) is proposed to precisely model the details of spatial changes and the distribution of multiple change categories for RSIs. The encoder of the proposed S3CDRNet is based on a Siamese structure, where a shallow convolutional neural network (CNN) followed by a transformer are used to extract the local-global change features. A precise semantic change perception module (PSCPM) based on large kernel convolution is then introduced to enhance the weak semantic changes. The decoder consists of several deconvolution layers to restore the original resolution, and a pixelwise semantic change map is acquired. To further distinguish the inherent imbalanced change types, an adaptive category-balanced semi-supervised learning (ACBSS) strategy is developed to better use the abundant class distribution information from the unlabeled image pairs. In the experiments conducted in this study, we made an in-depth study of multiple SCD based on three public datasets: the SECOND dataset, the Landsat-SCD dataset, and the Hi-UCD mini dataset. The results show the potential of the proposed method under scenarios with various change classes and an imbalanced change class problem, compared with the state-of-the-art SCD methods.