首个面向遥感图像低级视觉感知的诊断基准，让大模型学会识别并描述遥感图像失真。
SenseBench: A Benchmark for Remote Sensing Low-Level Visual Perception and Description in Large Vision-Language Models
- 构建基于物理的分级分类体系，覆盖22类遥感退化类型
- 测试29个主流大模型，发现其对遥感退化存在误判与描述偏差
- 提供可解释的诊断数据，适合遥感与多模态模型研究者使用
低级视觉感知是可靠遥感（RS）图像分析的基础，但现有图像质量评估（IQA）方法输出不可解释的标量分数，无法刻画由物理机制驱动的遥感退化，与遥感专家的实际诊断需求严重脱节。尽管视觉-语言模型（VLMs）可通过语言描述实现可解释的IQA，但其视觉先验严重偏向地面自然图像。因此，现有方法尚不足以验证VLMs能否克服领域差距以感知并描述遥感退化。为此，我们提出 extbf{SenseBench}，首个专用于遥感低级视觉感知与描述的诊断基准。基于物理驱动的分层分类体系，统一非参考与参考两种范式，包含超过10,000个精心标注的样本，覆盖6大类、22细粒度的遥感退化类别。设计两种互补评估协议：客观低级视觉 extit{感知} 与主观诊断 extit{描述}。对29个前沿VLMs的全面评估揭示了领域先验偏移、多退化混淆、 extit{流畅性错觉} 及 extit{感知-描述倒置} 等现象。我们期望 SenseBench 能为遥感低级感知中VLMs的发展提供可靠的评测平台与高质量诊断数据。代码与数据集见：https://github.com/Zhong-Chenchen/SenseBench。
原文摘要 · Abstract (English)
Low-level visual perception underpins reliable remote sensing (RS) image analysis, yet current image quality assessment (IQA) methods output uninterpretable scalar scores rather than characterizing physics-driven RS degradations, deviating markedly from the diagnostic needs of RS experts. While Vision-Language Models (VLMs) present a compelling alternative by delivering language-grounded IQA, their visual priors are heavily biased toward ground-level natural images. Consequently, whether VLMs can overcome this domain gap to perceive and articulate RS artifacts remains insufficiently studied. To bridge this gap, we propose \textbf{SenseBench}, the first dedicated diagnostic benchmark for RS low-level visual perception and description. Driven by a physics-based hierarchical taxonomy that unifies both non-reference and reference-based paradigms, SenseBench features over 10K meticulously curated instances across 6 major and 22 fine-grained RS degradation categories. Specifically, two complementary protocols are designed for evaluation: objective low-level visual \textit{perception} and subjective diagnostic \textit{description}. Comprehensive evaluation of 29 state-of-the-art VLMs reveals not only skewed domain priors and multi-distortion collapse, but also \textit{fluency illusion} and a \textit{perception-description inversion} effect. We hope SenseBench provides a robust evaluation testbed and high-quality diagnostic data to advance the development of VLMs in RS low-level perception. Code and datasets are available \href{https://github.com/Zhong-Chenchen/SenseBench}{\textcolor{blue}{here}}.
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