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原文摘要

While recent foundation models for remote sensing segmentation have shown notable progress, they still fall short in processing diverse multi-modal inputs, synergizing complementary prompt types, and leveraging semantic hierarchies. To address these limitations, we introduce SkySense-VITA, a unified in-context segmentation model, which synergistically processes both optical and Synthetic Aperture Radar (SAR) imagery using VIsual, TextuAl, or fused prompts. Based on a novel prompt-and-prediction decoupling strategy, we propose the VITA-Former and VITA-Decoder to decouple multi-modal prompt fusion and prediction process, allowing the model to flexibly support visual-only, textual-only, and fused prompt modes. We train SkySense-VITA with a progressive two-stage strategy: a first stage of Image-Level Alignment Pretraining featuring optical-SAR alignment, and a second stage of Pixel-Level In-context Pretraining using Semantic Granularity Annealing (SGA), a coarse-to-fine curriculum that enables robust hierarchical learning. To support this training, we introduce our new large-scale, multi-modal Sky-VT-300k dataset. Extensive experiments show SkySense-VITA establishes a new state-of-the-art (SOTA) on 18 datasets, with an average performance lead of over 10% mean Intersection over Union (mIoU).