Collaborative Self-Perception Network Architecture for Hyperspectral Image Change Detection

Despite the great advantages in deep feature representation when dealing with change detection (CD) problem, the designs of neural networks were time-consuming processes of trial and error. In addition, the traditional CD methods based on deep neural networks (DNN) only deal with one dataset at a time, which has limited learning knowledge and undoubtedly fails to take advantage of the common characteristics among similar datasets. For hyperspectral images (HSIs) obtained by the same sensor, the spectral information has a similar physical meaning (radiance or reflectivity). To utilize the inherent similarity within hyperspectra for learning a robust difference signature, a collaborative analysis framework with self-perception network architecture (SPNA-CA) is proposed to efficiently learn from multiple datasets and leverage their synergy. Different network architecture searching tasks are established for each dataset pertinently, in which the evolutionary multitasking self-perception network architecture (SPNA) method is designed for exploring effective and reasonable network architectures. Besides, a cross-task knowledge transfer mechanism (CKTM) is proposed to transfer excellent network architecture information, which improves the efficiency of the collaborative analysis framework. Experimental results confirm the effectiveness of collaborative analysis for solving HSI-CD problems among multiple datasets.