MDENet: Multidomain Differential Excavating Network for Remote Sensing Image Change Detection

• Published in: IEEE transactions on geoscience and remote sensing Vol. 62; pp. 1 - 11
• Main Authors: Liu, Jinyang, Li, Shutao, Dian, Renwei, Song, Ze, Kang, Xudong
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adaptability; Change detection; Convolution; Data mining; Deep learning; Earth surface; Feature extraction; Frequency dependence; Frequency-domain analysis; Image enhancement; Localization; Logic gates; Remote sensing; remote sensing image change detection; Transformers
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3413677

Remote sensing image change detection can analyze alterations on the Earth's surface within a specific region. However, the accuracy of change detection has consistently been hindered by the style differences in captured images caused by seasonal or lighting variations, as well as the challenge of distinguishing similar features between the background and foreground in the scene. To this end, a multidomain differential excavating network (MDENet) for change detection is introduced. Using the novel multidomain differential collaboration module (MDCM) to precisely capture object features on the frequency and spatial domains across diverse temporal domains, it enables simultaneous querying of global and local change information. Moreover, the multineighborhood frequency gate attention (MFGatt) is devised to eliminate the impact of image style relevance information and consolidate attention toward object localization, thereby enhancing the adaptability of the network to variations in image style. Extensive experiments have illustrated that our proposed network achieves better detection accuracy compared with current state-of-the-art (SOTA) methods on various datasets.