HyperImageNet: A Large-Scale High-Spatial Resolution Hyperspectral Imagery Classification Benchmark
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Abstract (arXiv)
We present HyperImageNet, a large-scale benchmark for fine-grained hyperspectral land-cover understanding. The dataset contains 26,084 airborne hyperspectral image patches with 224 spectral bands and 138 fine-grained land-cover categories. Unlike existing datasets, HyperImageNet provides raw imagery, pixel-level semantic labels, and object-level instance masks, supporting both semantic and instance segmentation. Furthermore, we establish an open-environment benchmark with strict spatial separation to evaluate representative methods and the HyperFree foundation model. Experimental results demonstrate the effectiveness of HyperImageNet for fine-grained hyperspectral understanding and open-environment remote sensing research.
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References · 9
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2023Segment Anythingcited by 8,158
- 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
- 2019HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classificationcited by 91
- 2020FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classificationcited by 27
- 2023HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methodscited by 26
- 2024HSIGene: A Foundation Model For Hyperspectral Image Generationcited by 21
- 2025HyperFree: A Channel-adaptive and Tuning-free Foundation Model for Hyperspectral Remote Sensing Imagerycited by 16
- 2026SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretrainingcited by 2
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