REST: Holistic Learning for End-to-End Semantic Segmentation of Whole-Scene Remote Sensing Imagery

Semantic segmentation of remote sensing imagery (RSI) is a fundamental task that aims at assigning a category label to each pixel. To pursue precise segmentation with one or more fine-grained categories, semantic segmentation often requires holistic segmentation of whole-scene RSI (WRI), which is normally characterized by a large size. However, conventional deep learning methods struggle to handle holistic segmentation of WRI due to the memory limitations of the graphics processing unit (GPU), thus requiring to adopt suboptimal strategies such as cropping or fusion, which result in performance degradation. Here, we introduce the Robust End-to-end semantic Segmentation architecture for whole-scene remoTe sensing imagery (REST). REST is the first intrinsically endtoend framework for truly holistic segmentation of WRI, supporting a wide range of encoders and decoders in a plugandplay fashion. It enables seamless integration with mainstream semantic segmentation methods, and even more advanced foundation models. Specifically, we propose a novel spatial parallel interaction mechanism (SPIM) within REST to overcome GPU memory constraints and achieve global context awareness. Unlike traditional parallel methods, SPIM enables REST to process a WRI effectively and efficiently by combining parallel computation with a divideandconquer strategy. Both theoretical analysis and experiments demonstrate that REST attains nearlinear throughput scalability as additional GPUs are employed. Extensive experiments demonstrate that REST consistently outperforms existing cropping-based and fusion-based methods across a variety of scenarios, ranging from single-class to multi-class segmentation, from multispectral to hyperspectral imagery, and from satellite to drone platforms. The robustness and versatility of REST are expected to offer a promising solution for the holistic segmentation of WRI, with the potential for further extension to large-size medical imagery segmentation.