F3Net: Feature Filtering Fusing Network for Change Detection of Remote Sensing Images

Abstract

Change detection of remote sensing images is an essential method for observing changes on the Earth's surface. Deep learning can efficiently process remote sensing images. However, shallow features in remote sensing data from different time are inherently inconsistent. During the feature extraction stage, these shallow features are mapped onto different dimensional feature maps, giving rise to noise information. Existing algorithms are ineffective in dealing with noise effectively. This can lead to detection results being influenced by shallow features noise information, resulting in fake detections. To address this issue, feature filtering fusing network (F3Net) is proposed in this article. In F3Net, feature filtering and aggregation (FFA) module is designed to integrate bitemporal remote sensing features, which initially filters out noise information from different temporal domains. In addition, the channel feature difference fusion (CFDF) module is introduced to fuse high-dimensional features. Within CFDF, channel information filtering convolution is utilized to filter out noise information from high-dimensional feature channels across multiple receptive fields. In order to verify the performance of F3Net, comparative experiments were conducted on multiple public datasets with other state-of-the-art models, and F3Net achieved the best performance.

Keywords

• Change detection
• deep learning
• multiple receptive fields
• noise information