[2604.08884] HM-Bench: A Comprehensive Benchmark for Multimodal Large Language Models in Hyperspectral Remote Sensing

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.08884 (cs) [Submitted on 10 Apr 2026] Title:HM-Bench: A Comprehensive Benchmark for Multimodal Large Language Models in Hyperspectral Remote Sensing Authors:Xinyu Zhang, Zurong Mai, Qingmei Li, Zjin Liao, Yibin Wen, Yuhang Chen, Xiaoya Fan, Chan Tsz Ho, Bi Tianyuan, Haoyuan Liang, Ruifeng Su, Zihao Qian, Juepeng Zheng, Jianxi Huang, Yutong Lu, Haohuan Fu View a PDF of the paper titled HM-Bench: A Comprehensive Benchmark for Multimodal Large Language Models in Hyperspectral Remote Sensing, by Xinyu Zhang and 15 other authors View PDF HTML (experimental) Abstract:While multimodal large language models (MLLMs) have made significant strides in natural image understanding, their ability to perceive and reason over hyperspectral image (HSI) remains underexplored, which is a vital modality in remote sensing. The high dimensionality and intricate spectral-spatial properties of HSI pose unique challenges for models primarily trained on RGB this http URL address this gap, we introduce Hyperspectral Multimodal Benchmark (HM-Bench), the first benchmark designed specifically to evaluate MLLMs in HSI understanding. We curate a large-scale dataset of 19,337 question-answer pairs across 13 task categories, ranging from basic perception to spectral reasoning. Given that existing MLLMs are not equipped to process raw hyperspectral cubes natively, we propose a dual-modality evaluation framework that transforms HSI data int...