README.md

metadata

license: apache-2.0
tags:
  - vision
  - hyperspectral
  - foundation-model
  - image-segmentation
pipeline_tag: image-feature-extraction

HyperVision: Channel-Adaptive Ground-Based Hyperspectral Vision Foundation Models

This is the official repository for the paper "HyperVision: Channel-Adaptive Ground-Based Hyperspectral Vision Foundation Models". https://arxiv.org/abs/2605.17286

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🚀 News:

• [2026-07-02] Pre-trained checkpoints are now available! You can download them from Hugging Face Checkpoints.

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📖 Abstract

While hyperspectral imaging provides rich spatial-spectral information across hundreds of narrow wavelength bands for precise material identification, ground-based hyperspectral pre-trained backbones remain absent, constrained by varying spectral configurations across sensors, the scarcity and inconsistency of labels, and the limited scale and scene diversity of existing datasets. To address these challenges and enable universal perception, we propose HyperVision, the first ground-based hyperspectral pre-trained backbone. First, to handle varying spectral configurations, HyperVision adopts a channel-adaptive dynamic embedding mechanism to map heterogeneous inputs into a unified token space. Second, to address the scarcity and inconsistency of labels, we introduce a multi-source pseudo-labeling method that fuses semantic representations from both spatial structures generated by SAM2 and fine-grained spectral material information extracted by HyperFree. Third, to compensate for limited dataset scale and enrich scene diversity, a cross-modal knowledge distillation mechanism is utilized to transfer rich semantic representations from a pre-trained RGB vision model to our hyperspectral backbone. Pre-trained on a collection of 15k images from 26 diverse ground-based datasets, HyperVision demonstrates exceptional generalization. Requiring only efficient head-only adaptation without adjusting backbone parameters, it achieves state-of-the-art performance compared to task-specific methods across three downstream tasks under varying sensor configurations, yielding up to a 16.3% relative improvement in hyperspectral semantic segmentation $\text{Acc}_{\text{M}}$, a 2.1% relative gain in object tracking AUC, and a 35.5% reduction in salient object detection MAE. The source code and pre-trained model will be publicly available online.

🛠️ Main Code (HyperVision)

The core architecture and model initialization utilities are located under the HyperVision directory.

To initialize the model, you can use the model builder functions from HyperVision/build_HyperVision.py. The pre-trained checkpoints can be downloaded from Hugging Face Checkpoints.

We support different model configurations:

• HyperVision-B: build_HyperVision_b
• HyperVision-L: build_HyperVision_l
• HyperVision-H (Default): build_HyperVision_h

Python Example:

import torch
from HyperVision import build_HyperVision_h, HyperVision_Predictor
from hyperspectral_image_reader.read_dataset_image import load_hypervision_matrix
from hyperspectral_image_reader.hyperspectral_pipelines import LoadHyperspectralImage

device = "cuda" if torch.cuda.is_available() else "cpu"
image_size = 512

# 1. Build the HyperVision model (HyperVision-H configuration shown below)
model = build_HyperVision_h(
    checkpoint="path/to/hypervision_distilled_h.pth",             # Optional checkpoint path (.pth weights)
    image_size=512,                                         # Input resolution
    vit_patch_size=16,                                      # Patch size of the backbone
    encoder_global_attn_indexes=[15, 23, 31],               # Layers using global attention (can be adjusted for debugging/tuning)
    merge_indexs=[8, 32],                                   # Layers doing patch merging (can be adjusted for debugging/tuning)
    class_number=-1                                         # Number of classes for mask decoder
)
model = model.to(device)
model.eval()

# 2. Use HyperVision_Predictor to manage preprocessing and feature extraction
predictor = HyperVision_Predictor(model)

# 3. Load the preprocessed HSI image as a numpy array using load_hypervision_matrix
# Preprocessed shape: (H_ori, W_ori, C_hsi) with values in range [0, 255]
img_matrix = load_hypervision_matrix("path/to/image.mat", dataset_name="harvard")

# 4. Automatically retrieve the wavelengths list from the dataset pipeline config
loader = LoadHyperspectralImage(dataset_type="harvard")
wavelengths = loader.wavelengths

# 5. Call predictor.set_image to preprocess the image and extract features (calculating embeddings)
# GSD represents Ground Sampling Distance (defaults to 0.01)
with torch.no_grad():
    predictor.set_image(img_matrix, test_mode=False, spectral_lengths=wavelengths, GSD=0.01)

# 6. Extract multi-scale features from the predictor
multi_stage_features = predictor.multi_scale_features

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📊 Dataset Reading & Processing

We provide read_dataset_image.py to read and preprocess hyperspectral images from various datasets. The script utilizes the custom data pipeline class LoadHyperspectralImage from configs/hypervision/hyperspectral_pipelines.py to normalize the images (scaled to [0, 255]) and reshape them into the format expected by HyperVision: (H_ori, W_ori, C_hsi).

Python API Usage:

from hyperspectral_image_reader.read_dataset_image import load_hypervision_matrix

# Load and preprocess HSI matrix
# dataset_name must be one of the supported dataset identifiers (e.g., 'harvard', 'arad_1k_31')
img_matrix = load_hypervision_matrix("path/to/image.mat", dataset_name="harvard")
print("Processed shape (H, W, C):", img_matrix.shape)

Command Line Interface:

You can also run the script from the command line to load an HSI image and optionally save the processed matrix as a .npy file:

python hyperspectral_image_reader/read_dataset_image.py --path /path/to/image.mat --dataset harvard --output output.npy

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🗃️ Supported Datasets

Below is a summary of the ground-based hyperspectral datasets supported by our pipeline, along with their respective keys to be used for the --dataset parameter:

Dataset Name	--dataset Key	# Bands	Wavelengths	# Images	Expected Extension	File Loader Detail / Required Accompanying Files
50 Outdoor	fiftyoutdoor	33	400–720 nm	50	.mat	Custom Mat loader
Agricultural Plant	aphid	237	436–965 nm	361	.npy	NumPy array loader
ARAD1K16	arad_1k_16	16	400–1000 nm	950	.mat	HDF5 H5PY loader
ARAD1K31	arad_1k_31	31	400–700 nm	949	.mat	HDF5 H5PY loader
CAVE	cave	31	400–700 nm	32	.mat	Mat loader
DeepHS-NIR	deephsnir	252	950–1700 nm	718	.bin	ENVI binary (requires corresponding .hdr file in the same directory)
DeepHS-VIS	deephsvis	224	400–1000 nm	3405	.bin	ENVI binary (requires corresponding .hdr file in the same directory)
DeepHS-VISCOR	deephsviscor	249	400–1000 nm	1566	.bin	ENVI binary (requires corresponding .hdr file in the same directory)
Harvard	harvard	31	420–720 nm	77	.mat	Mat loader
HOT-2024-NIR	hotnir	25	665–960 nm	477	.png	PNG frame loader (requires corresponding false-color .jpg file)
HOT-2024-RedNIR	hotrednir	15	600–850 nm	348	.png	PNG frame loader (requires corresponding false-color .jpg file)
HOT-2024-VIS	hotvis	16	470–600 nm	1070	.png	PNG frame loader (requires corresponding false-color .jpg file)
HSI Drive v2.0	hsidrive20	25	600–975 nm	752	.npy	NumPy loader (requires corresponding pseudocolor .png file)
HSI Road	hsiroad	25	600–960 nm	380	.tif	TIF image loader
HSODBIT v2	hsodbitv2	200	400–1000 nm	500	.mat	HDF5 H5PY loader (requires corresponding color .jpg file)
HSSOD	hs_sod	81	380–720 nm	60	.h5	HDF5 H5PY loader (requires corresponding color .jpg file)
HyKo v2-NIR	hykov2nir	25	600–975 nm	78	.mat	Mat loader
HyKo v2-VIS	hykov2vis	16	470–630 nm	163	.mat	Mat loader
HyperBlood	hyperblood	128	377–1046 nm	14	.mat	Custom Mat loader
HyperDrive-VNIR	hyperdrivevnir	24	660–900 nm	504	.npz	NumPy archive (must contain cube.npy file inside)
HyperspectralCity v2	hyperspectralcityv2	128	450–950 nm	1330	.hsd	HSD raw data loader
ICVL	icvl	31	400–700 nm	187	.h5	HDF5 H5PY loader
LIB-HSI	libhsi	204	400–1000 nm	393	.hdr	ENVI header (requires corresponding raw binary data .raw/.dat file)
UM-EMM	umemm	33	400–720 nm	3	.mat	Mat loader
UM-LD 2015	umld2015	33	400–720 nm	20	.mat	Mat loader
UM-NS 2002	umns2002	31	410–710 nm	8	.mat	Mat loader
UM-NS 2004	umns2004	33	400–720 nm	10	.mat	Mat loader
UM-OS	umos	33	400–720 nm	50	.mat	Mat loader
UM-RI 2015	umri2015	33	400–720 nm	33	.mat	Mat loader
Virginia Tech Tree	virginia_tech_tree	420	400–1000 nm	51	.hdr	ENVI header (requires corresponding raw binary data file)
Apple Fire Blight	vnihdhiatlimafb	204	400–1000 nm	420	.hdr	ENVI header (requires corresponding raw binary data file)

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📝 Citation

If you find our work or this project helpful, please consider citing our paper:

@misc{fu2026hypervision,
      title={HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone}, 
      author={Guanyiman Fu and Jingtao Li and Zihang Cheng and Zhuanfeng Li and Diqi Chen and Yan Xu and Xiangyu Liu and Fengchao Xiong and Jianfeng Lu and Chengrong Chen and Jun Zhou},
      year={2026},
      eprint={2605.17286},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2605.17286}, 
}

(Note: Citation details will be updated upon official publication)