HAD100 DATASET
A Beachmark Dataset for Hyperspectral Anomaly Detection
Zhaoxu Li, Yingqian Wang, Chao Xiao, Qiang Ling, Zaiping Lin, Wei An
Zhaoxu Li, Yingqian Wang, Chao Xiao, Qiang Ling, Zaiping Lin, Wei An
HAD100 is a new benchmark dataset for the hyperspectral anomaly detection task. This dataset contains 100 test scenes with various targets and backgrounds. The test scenes are downloaded from AVIRIS-NG and uniformly cropped to patches of size 64 ✕ 64. Furthermore, the HAD100 dataset provides two training sets which are downloaded from AVIRIS-NG and AVIRIS-Classic, respectively. The AVIRIS-NG training set contains 260 anomaly-free hyperspectral imamgs, while the AVIRIS-Classic training set contains 262 anomaly-free hyperspectral imamgs.
For more detail, please refer to our paper.
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HAD100 dataset can be downloaded from Baidu Disk (code: 1234) or Google Drive.
After unzipping the downloaded HAD100.zip, you can run the following command to obtain the dataset used in our paper:
  python main.py
We have compared 17 hyperspectral anomaly detection methods plus two variants, and the evaluation results are shown below.
All the test results and evaluation code can be downloaded from Baidu Disk (code: 1234)or Google Drive.
If this dataset is helpful, please cite our following paper:
@ARTICLE{10073635, author={Li, Zhaoxu and Wang, Yingqian and Xiao, Chao and Ling, Qiang and Lin, Zaiping and An, Wei}, journal={IEEE Transactions on Geoscience and Remote Sensing}, title={You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly Detection}, year={2023}, volume={61}, number={}, pages={1-18}, doi={10.1109/TGRS.2023.3258067}}
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