Summary

This dataset contains two hyperspectral and one multispectral anomaly detection images, and their corresponding binary pixel masks. They were initially used for real-time anomaly detection in line-scanning, but they can be used for any anomaly detection task.

They are in .npy file format (will add tiff or geotiff variants in the future), with the image datasets being in the order of (height, width, channels). The SNP dataset was collected using sentinelhub, and the Synthetic dataset was collected from AVIRIS. The Python code used to analyse these datasets can be found at: https://github.com/WiseGamgee/HyperAD

How to Get Started

All that is needed to load these datasets is Python (preferably 3.8+) and the NumPy package. Example code for loading the Beach Dataset if you put it in a folder called "data" with the python script is:

import numpy as np

# Load image file
hsi_array = np.load("data/beach_hsi.npy")
n_pixels, n_lines, n_bands = hsi_array.shape
print(f"This dataset has {n_pixels} pixels, {n_lines} lines, and {n_bands}.")

# Load image mask
mask_array = np.load("data/beach_mask.npy")
m_pixels, m_lines = mask_array.shape
print(f"The corresponding anomaly mask is {m_pixels} pixels by {m_lines} lines.")

Citing the Datasets

If you use any of these datasets, please cite the following paper:

@article{garske2024erx,
  title={ERX - a Fast Real-Time Anomaly Detection Algorithm for Hyperspectral Line-Scanning},
  author={Garske, Samuel and Evans, Bradley and Artlett, Christopher and Wong, KC},
  journal={arXiv preprint arXiv:2408.14947},
  year={2024},
}

If you use the beach dataset please cite the following paper as well (original source):

@article{mao2022openhsi,
  title={OpenHSI: A complete open-source hyperspectral imaging solution for everyone},
  author={Mao, Yiwei and Betters, Christopher H and Evans, Bradley and Artlett, Christopher P and Leon-Saval, Sergio G and Garske, Samuel and Cairns, Iver H and Cocks, Terry and Winter, Robert and Dell, Timothy},
  journal={Remote Sensing},
  volume={14},
  number={9},
  pages={2244},
  year={2022},
  publisher={MDPI}
}