HyperHazeOff: Hyperspectral Remote Sensing Image Dehazing Benchmark

Hyperspectral remote sensing imagery provides invaluable information for environmental and agricultural monitoring but remains highly susceptible to atmospheric haze. We introduce the first comprehensive benchmark for hyperspectral image dehazing featuring paired real-world hazy and haze-free remote sensing data. The Remote sensing Real-world Hyperspectral Paired Dehazing Image Dataset (RRealHyperPDID) includes 110 scenes with naturally occurring haze and corresponding clear references, enabling reliable evaluation of dehazing algorithms across both hyperspectral and RGB domains. To support model training and ablation studies, we present the Remote Sensing Synthetic Hyperspectral Paired Dehazing Image Dataset (RSyntHyperPDID), comprising 2,616 synthetically generated hazy-clear pairs. The benchmark also provides agricultural field delineation annotations, allowing assessment of dehazing effects on downstream remote sensing tasks. Extensive experiments with six state-of-the-art methods reveal fundamental limitations of existing hyperspectral dehazing approaches and highlight the need for realistic, domain-consistent data. Models trained on RSyntHyperPDID demonstrate better generalization to real hazy data, establishing a new baseline for hyperspectral dehazing.