Causal Invariant Representation Learning Based on Style Intervention Identity Regularization for Remote Sensing Image

An intelligent understanding model of a remote sensing image will present different visual representations of the same object in the remote sensing image, under the interference of offset factors, such as weather and season. This variability adversely affects the generalization ability of the model; therefore, an open challenge is how to learn invariant features. These features can maintain the model’s generalization ability under various imaging conditions and given the spatiotemporal heterogeneity of the object. From the causal point of view, each object of the remote sensing image is decomposed into two parts, content and style; content is the causal variable of the target task, which is robust and stable, and style is the noncausal variable, which is more susceptible to the interference of bias factors and is volatile. This paper proposes a remote sensing imagery invariance representation method based on style intervention identity regularization (ICRNet). Within the contrastive learning framework, style transformation based on a diffusion model is introduced as an intervention factor to modify the style of remote sensing images. In addition, style intervention identity regularization is designed, employing symmetric JS divergence as the similarity measure. This approach forces remote sensing images with identical content but different styles to have a similar impact on the target task, thereby enabling learning invariant content representations while ignoring style representations. The experiments were conducted on three remote sensing semantic segmentation datasets. The results reveal that ICRNet learns invariant content representations better than alternative representation learning approaches based on contrast learning. Additionally, ICRNet exhibits higher generalizability under seasonal bias. The source code is available at https://github.com/GeoX-Lab/ICRNet.