DOI: 10.5281/zenodo.16737351
title: HyCamUX - Airborne Hyperspectral Camouflage and UXO Reconnaissance Dataset

description: Hyperspectral Dataset 'HyCamUX'

1. Dataset Overview

This dataset contains hyperspectral imagery collected for the purpose of hyperspectral anomaly detection in reconnaissance missions.  All data is provided in ENVI format (.hdr + .dat).

IMPORTANT: All hyperspectral data is NOT atmospheric corrected and only contains radiometric values!

2. Directory Structure

The dataset is organized as follows:



test_and_training_config.tar/ (contains the sample names and the order of the train and test configurations in JSON format for the two datasets used in the original publication)

Hyspec_02_dataset_band-selection_afx17_ED-Camo_height-step-05_width-step033_zip.tar.zst/ (contains the hyperspectral datasets):


├── dataset_1/│   ├── 2023-09-04_Summer_AFX17_ED-Camo/│   │   ├── image_001.dat│   │   ├── image_001.hdr│   │   ├── image_001.png│   │   ├── image_002.dat│   │   ├── image_002.hdr│   │   ├── image_002.png│   │   └── ...│   ├── 2023-11-08_Autumn_AFX17_Camo-ED/│   │   ├── image_001.dat│   │   ├── image_001.hdr│   │   ├── image_001.png│   │   ├── image_002.dat│   │   ├── image_002.hdr│   │   ├── image_002.png│   │   └── ...│   ├── 2024-03-25_Winter_AFX17_Camo-ED/│   │   ├── image_001.dat│   │   ├── image_001.hdr│   │   ├── image_001.png│   │   ├── image_002.dat│   │   ├── image_002.hdr│   │   ├── image_002.png│   │   └── ...│   ├── 2024-05-22_Spring_AFX17_Camo-ED/│   │   ├── image_001.dat│   │   ├── image_001.hdr│   │   ├── image_001.png│   │   ├── image_002.dat│   │   ├── image_002.hdr│   │   ├── image_002.png│   │   └── ...│   └── labelmap.txt├── dataset_2/│   ├── 2023_summer_camo_ED_afx17_storkow/│   │   ├── image_001.dat│   │   ├── image_001.hdr│   │   ├── image_001.png│   │   ├── image_002.dat│   │   ├── image_002.hdr│   │   ├── image_002.png│   │   └── ...│   ├── 2024_09_04_Storkow_AFX17_Session_01/│   │   ├── image_001.dat│   │   ├── image_001.hdr│   │   ├── image_001.png│   │   ├── image_002.dat│   │   ├── image_002.hdr│   │   ├── image_002.png│   │   └── ...│   └── labelmap.txt└── README.txt

3. File Descriptions



`images`: Contains the hyperspectral image cubes in ENVI Standard format. Each image consists of a `.dat` file (raw binary), a `.hdr` file (header with metadata) and a label in .png format.

`labelmap`: Contains target id's and corresponding RGB lable color.


4. Image Format

The format of the hyperspectral images is specified as following:



Format: ENVI Standard

Data type: 32-bit float (`data_type = 4`)

Shape: (spatial x × spatial y × bands) = (e.g., 550 × 550 × 224)

GSD: Folder '2023_summer_camo_ED_afx17_storkow': 0.055m, the remaining data: 0.065 m

Wavelength range: 935–1720 nm

Spectral resolution: 5.5 nm (optional)

interleave = bil


5. Wavelengths

Wavelengths for each band are stored in the `.hdr` file as:

- wavelength = {  935.610000,  939.060000,  942.520000,  945.980000,  949.430000,  952.890000,  956.350000,  959.810000,  963.270000,  966.730000,  970.190000,  973.650000,  977.110000,  980.580000,  984.040000,  987.510000,  990.970000,  994.430000,  997.900000, 1001.370000, 1004.830000, 1008.300000, 1011.770000, 1015.240000, 1018.710000, 1022.180000, 1025.650000, 1029.120000, 1032.590000, 1036.060000, 1039.530000, 1043.000000, 1046.480000, 1049.950000, 1053.430000, 1056.900000, 1060.380000, 1063.850000, 1067.330000, 1070.810000, 1074.290000, 1077.760000, 1081.240000, 1084.720000, 1088.200000, 1091.680000, 1095.170000, 1098.650000, 1102.130000, 1105.610000, 1109.100000, 1112.580000, 1116.070000, 1119.550000, 1123.040000, 1126.520000, 1130.010000, 1133.500000, 1136.990000, 1140.470000, 1143.960000, 1147.450000, 1150.940000, 1154.430000, 1157.930000, 1161.420000, 1164.910000, 1168.400000, 1171.900000, 1175.390000, 1178.890000, 1182.380000, 1185.880000, 1189.370000, 1192.870000, 1196.370000, 1199.870000, 1203.370000, 1206.870000, 1210.370000, 1213.870000, 1217.370000, 1220.870000, 1224.370000, 1227.870000, 1231.380000, 1234.880000, 1238.390000, 1241.890000, 1245.400000, 1248.900000, 1252.410000, 1255.920000, 1259.420000, 1262.930000, 1266.440000, 1269.950000, 1273.460000, 1276.970000, 1280.480000, 1283.990000, 1287.510000, 1291.020000, 1294.530000, 1298.050000, 1301.560000, 1305.080000, 1308.590000, 1312.110000, 1315.620000, 1319.140000, 1322.660000, 1326.180000, 1329.700000, 1333.220000, 1336.740000, 1340.260000, 1343.780000, 1347.300000, 1350.820000, 1354.350000, 1357.870000, 1361.390000, 1364.920000, 1368.440000, 1371.970000, 1375.500000, 1379.020000, 1382.550000, 1386.080000, 1389.610000, 1393.140000, 1396.670000, 1400.200000, 1403.730000, 1407.260000, 1410.790000, 1414.320000, 1417.860000, 1421.390000, 1424.920000, 1428.460000, 1431.990000, 1435.530000, 1439.070000, 1442.600000, 1446.140000, 1449.680000, 1453.220000, 1456.760000, 1460.300000, 1463.840000, 1467.380000, 1470.920000, 1474.460000, 1478.010000, 1481.550000, 1485.090000, 1488.640000, 1492.180000, 1495.730000, 1499.270000, 1502.820000, 1506.370000, 1509.910000, 1513.460000, 1517.010000, 1520.560000, 1524.110000, 1527.660000, 1531.210000, 1534.760000, 1538.320000, 1541.870000, 1545.420000, 1548.980000, 1552.530000, 1556.090000, 1559.640000, 1563.200000, 1566.750000, 1570.310000, 1573.870000, 1577.430000, 1580.990000, 1584.550000, 1588.110000, 1591.670000, 1595.230000, 1598.790000, 1602.350000, 1605.920000, 1609.480000, 1613.040000, 1616.610000, 1620.170000, 1623.740000, 1627.310000, 1630.870000, 1634.440000, 1638.010000, 1641.580000, 1645.150000, 1648.710000, 1652.290000, 1655.860000, 1659.430000, 1663.000000, 1666.570000, 1670.140000, 1673.720000, 1677.290000, 1680.870000, 1684.440000, 1688.020000, 1691.590000, 1695.170000, 1698.750000, 1702.330000, 1705.910000, 1709.490000, 1713.070000, 1716.650000, 1720.230000}- fwhm = { 3.460000, 3.460000, 3.460000, 3.460000, 3.460000, 3.460000, 3.460000, 3.460000, 3.460000, 3.460000, 3.460000, 3.460000, 3.460000, 3.460000, 3.460000, 3.460000, 3.460000, 3.470000, 3.470000, 3.470000, 3.470000, 3.470000, 3.470000, 3.470000, 3.470000, 3.470000, 3.470000, 3.470000, 3.470000, 3.470000, 3.470000, 3.470000, 3.470000, 3.470000, 3.470000, 3.480000, 3.480000, 3.480000, 3.480000, 3.480000, 3.480000, 3.480000, 3.480000, 3.480000, 3.480000, 3.480000, 3.480000, 3.480000, 3.480000, 3.480000, 3.480000, 3.480000, 3.480000, 3.490000, 3.490000, 3.490000, 3.490000, 3.490000, 3.490000, 3.490000, 3.490000, 3.490000, 3.490000, 3.490000, 3.490000, 3.490000, 3.490000, 3.490000, 3.490000, 3.490000, 3.490000, 3.500000, 3.500000, 3.500000, 3.500000, 3.500000, 3.500000, 3.500000, 3.500000, 3.500000, 3.500000, 3.500000, 3.500000, 3.500000, 3.500000, 3.500000, 3.500000, 3.500000, 3.500000, 3.510000, 3.510000, 3.510000, 3.510000, 3.510000, 3.510000, 3.510000, 3.510000, 3.510000, 3.510000, 3.510000, 3.510000, 3.510000, 3.510000, 3.510000, 3.510000, 3.510000, 3.520000, 3.520000, 3.520000, 3.520000, 3.520000, 3.520000, 3.520000, 3.520000, 3.520000, 3.520000, 3.520000, 3.520000, 3.520000, 3.520000, 3.520000, 3.520000, 3.520000, 3.520000, 3.530000, 3.530000, 3.530000, 3.530000, 3.530000, 3.530000, 3.530000, 3.530000, 3.530000, 3.530000, 3.530000, 3.530000, 3.530000, 3.530000, 3.530000, 3.530000, 3.530000, 3.530000, 3.540000, 3.540000, 3.540000, 3.540000, 3.540000, 3.540000, 3.540000, 3.540000, 3.540000, 3.540000, 3.540000, 3.540000, 3.540000, 3.540000, 3.540000, 3.540000, 3.540000, 3.540000, 3.550000, 3.550000, 3.550000, 3.550000, 3.550000, 3.550000, 3.550000, 3.550000, 3.550000, 3.550000, 3.550000, 3.550000, 3.550000, 3.550000, 3.550000, 3.550000, 3.550000, 3.560000, 3.560000, 3.560000, 3.560000, 3.560000, 3.560000, 3.560000, 3.560000, 3.560000, 3.560000, 3.560000, 3.560000, 3.560000, 3.560000, 3.560000, 3.560000, 3.560000, 3.560000, 3.570000, 3.570000, 3.570000, 3.570000, 3.570000, 3.570000, 3.570000, 3.570000, 3.570000, 3.570000, 3.570000, 3.570000, 3.570000, 3.570000, 3.570000, 3.570000, 3.570000, 3.570000, 3.580000, 3.580000, 3.580000, 3.580000, 3.580000, 3.580000, 3.580000, 3.580000, 3.580000, 3.580000, 3.580000}

6. License

This project is licensed under Creative Commons Attribution Non Commercial Share Alike 4.0 International.

7. Citation

This dataset is part of the following publication:

@Article{s25196199,AUTHOR = {Eckel, Linda and Stütz, Peter},TITLE = {A Context-Adaptive Hyperspectral Sensor and Perception Management Architecture for Airborne Anomaly Detection},JOURNAL = {Sensors},VOLUME = {25},YEAR = {2025},NUMBER = {19},ARTICLE-NUMBER = {6199},URL = {https://www.mdpi.com/1424-8220/25/19/6199},ISSN = {1424-8220},DOI = {10.3390/s25196199}}

We would really appreciate it if you could cite this paper when you use the dataset. Thanks!

8. Acknowledgments

The authors sincerely thank Tobias Hupel for the support in creating the hyperspectral datasets.

8. Contact

Linda EckelUniversitaet der Bundeswehr MuenchenInstitut fuer FlugsystemeWerner-Heisenberg-Weg 3985579 Germanyl.eckel@unibw.dehttps://www.unibw.de/lft