HDFNet: Hierarchical Dynamic Fusion Network for Change Detection in Optical Aerial Images

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 8; p. 1440
• Main Authors: Zhang, Yi, Fu, Lei, Li, Ying, Zhang, Yanning
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 08.04.2021
• Subjects: aerial photography; area; Change detection; Convolution; data collection; Deep learning; dynamic convolution; exhibitions; hierarchical; image analysis; learning; multilevel supervision; Neural networks; remote sensing; Semantics; Supervision; Siam
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13081440

Accurate change detection in optical aerial images by using deep learning techniques has been attracting lots of research efforts in recent years. Correct change-detection results usually involve both global and local deep learning features. Existing deep learning approaches have achieved good performance on this task. However, under the scenarios of containing multiscale change areas within a bi-temporal image pair, existing methods still have shortcomings in adapting these change areas, such as false detection and limited completeness in detected areas. To deal with these problems, we design a hierarchical dynamic fusion network (HDFNet) to implement the optical aerial image-change detection task. Specifically, we propose a change-detection framework with hierarchical fusion strategy to provide sufficient information encouraging for change detection and introduce dynamic convolution modules to self-adaptively learn from this information. Also, we use a multilevel supervision strategy with multiscale loss functions to supervise the training process. Comprehensive experiments are conducted on two benchmark datasets, LEBEDEV and LEVIR-CD, to verify the effectiveness of the proposed method and the experimental results show that our model achieves state-of-the-art performance.