GaMPF: A Full-Scale Gated Message Passing Framework Based on Collaborative Estimation for VHR Remote Sensing Image Change Detection

With the maturity and popularization of high-performance sensor technology, it is now possible to acquire huge amounts of very high-resolution (VHR) remote sensing images. The change detection (CD) for VHR images is currently receiving special attention for remote sensing earth observation applications, however, as a hot research field, it needs to be studied in depth to improve the detection accuracy of fine changes. To this end, a full-scale gated message passing framework (GaMPF) based on collaborative estimation for VHR remote sensing image change detection is proposed in this paper. On one hand, the key embedding representation is generated for each feature map by means of the collaborative estimation (CE) strategy; On the other hand, grounded in timing analysis, bitemporal features are sent selectively on dual paths according to the full-scale gated (FsG) mechanism. Specifically, this framework consists of the following four components: 1) Taking shared-weights Siamese network as an encoder to extract multi-scale features; 2) Generate a set of shared compact bases under the CE strategy and infer the key embedding representations on the basis of the shared bases for feature maps at the same level, considering the representations as the gated switches; 3) FsG mechanism is used as the mode of message passing between bitemporal images, which guides the information can be transmitted simultaneously on both within-and cross-temporal paths. 4) Creating a stepwise dense fusion module (DFM) as a decoder for predicting the change map. Experimental results show that the GaMPF proposed in this paper outperforms existing SOTA methods, and is particularly good at detecting edges and small objects. The source code will be released at https://github.com/zxylnnu/GaMPF.