WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets for hyperspectral image classification

近岸水下目标检测遇见无人机载高光谱遥感：一种新型混合级对比学习框架与基准数据集

Nearshore Underwater Target Detection Meets UAV-borne Hyperspectral Remote Sensing: A Novel Hybrid-level Contrastive Learning Framework and Benchmark Dataset

针对近岸高光谱畸变导致的水下目标检测难题，提出融合对比学习、自步学习与可靠性聚类的HUCLNet，并构建ATR2-HUTD基准，实验性能领先。

星载高光谱云检测的域适应

Domain Adaptation for Satellite-Borne Hyperspectral Cloud Detection

针对星载高光谱云检测中的域差距问题，提出带宽高效的监督域适应算法，仅需传输1%的ResNet50权重即可在卫星上实现模型更新，提升新任务性能。

HyperSIGMA：高光谱智能理解基础模型

HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model

提出基于vision transformer的高光谱基础模型HyperSIGMA，通过引入稀疏采样注意力机制和光谱增强模块，结合45万张图像的预训练数据集，实现了跨任务与场景的统一解译及卓越的表示能力。

HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package

As with any physical instrument, hyperspectral cameras induce different kinds of noise in the acquired data. Therefore, Hyperspectral denoising is a crucial step for analyzing hyperspectral images (HSIs). Conventional computational methods rarely use GPUs to improve efficiency and are not fully open-source. Alternatively, deep learning-based methods are often open-source and use GPUs, but their training and utilization for real-world applications remain non-trivial for many researchers. Consequently, we propose HyDe: the first open-source, GPU-accelerated Python-based, hyperspectral image deno…

面向高光谱图像分类的内容驱动幅度-导数光谱互补学习

Content-driven Magnitude-Derivative Spectrum Complementary Learning for Hyperspectral Image Classification

针对高光谱图像分类中光谱幅度特征易致混淆的问题，提出基于幅度-导数双编码器的内容驱动光谱互补网络，结合逐点自适应融合模块与混合差异增强损失，在多个基准数据集上取得SOTA表现。