Adaptive Self-Paced Collaborative and 3-D Adversarial Multitask Network for Semantic Change Detection Using Zhuhai-1 Orbita Hyperspectral Remote Sensing Imagery

Abstract

In recent years, numerous change detection methodologies have been proposed, with a predominant focus on binary change detection. Furthermore, there exists a paucity of research addressing semantic change detection in scenarios where solely binary change labels are available. This article introduces a multitask network for semantic change detection. First, 3-D ResUnet model is employed to generate initial multitemporal land cover results through postclassification comparison. Subsequently, the multitask network, encompassing two subtasks—binary change detection and multitemporal semantic segmentation—is proposed. Specifically, the shared branch of the network employs 3-D residual blocks to extract joint spectral-spatial features. In the subsequent task-specific branch, a 3-D GAN is incorporated for the binary change detection task to enhance the discrimination ability of latent high-level features for changes. Novel adaptive self-paced learning and certainty-weighted focal loss are proposed for multitemporal semantic segmentation to mitigate adverse effects from noisy semantic labels by considering sample complexity and reliability in the network optimization process. Experiments conducted on the Orbita Hyperspectral dataset in the Xiong'an New Area demonstrate the superior performance of the proposed method, achieving 99.28% and 76.60% for overall accuracy and kappa, respectively. This outperformance is notable when compared to other methods, such as Str4 and Bi-SRNet, showing an increase of 39.82% and 54.17% for kappa. Moreover, comparative experiments on SECOND further confirm the advantage of the proposed method, achieving 54.62% for kappa and outperforming other comparative methods, such as Bi-SRNet (47.61%).

Keywords

• Change detection
• hyperspectral remote sensing
• multitask learning
• pseudolabel
• semantic change detection