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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 multi-task network for semantic change detection. First, 3D ResUnet model is employed to generate initial multi-temporal land cover results through post classification comparison (PCC). Subsequently, the multi-task network, encompassing two sub-tasks&#x2014;binary change detection and multi-temporal semantic segmentation&#x2014;is proposed. Specifically, the shared branch of the network employs 3D residual blocks to extract joint spectral-spatial features. In the subsequent task-specific branch, a 3D 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 multi-temporal 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&#x0027;an New Area demonstrate the superior performance of the proposed method, achieving 99.28&#x0025; and 76.60&#x0025; for Overall Accuracy (OA) and Kappa, respectively. This outperformance is notable when compared to other methods, such as Str4 and Bi-SRNet, showing an increase of 39.82&#x0025; and 54.17&#x0025; for Kappa. Moreover, comparative experiments on SECOND further confirm the advantage of the proposed method, achieving 54.62&#x0025; for Kappa and outperforming other comparative methos like Bi-SRNet (47.61&#x0025;).

ieee journal of selected topics in applied earth observations and remote sensing

Adaptive Self-paced Collaborative and 3D Adversarial Multi-task Network for Semantic Change Detection using Zhuhai-1 Orbita Hyperspectral Remote Sensing Imagery