Dataset Viewer
Auto-converted to Parquet Duplicate
text
stringlengths
14
30
wavelength reflectance
398.84201 8.576919266666666
401.067993 8.459760093333333
403.294006 8.668065266666657
405.519989 8.520397580000004
407.747009 8.176843479999986
409.972992 8.409002666666682
412.199005 8.682619035714296
414.424988 8.423828613333328
416.651001 8.166871871428576
418.877991 8.217964619999984
421.104004 8.388598600000003
423.329987 8.237796620000001
425.556 8.030114285714284
427.782013 7.968518179999998
430.009003 7.879112593333337
432.234985 7.723498499999997
434.460999 7.639173226666668
436.687012 7.512597599999999
438.912994 7.468180839999993
441.140015 7.3477144999999995
443.365997 7.315957164285714
445.59201 7.272879733333332
447.817993 7.26085352
450.044006 7.2597066266666666
452.270996 7.25
454.497009 7.181385842857143
456.722992 7.2
458.949005 7.205335821428572
461.174988 7.201333653333335
463.402008 7.222680053333334
465.627991 7.23
467.854004 7.249728657142857
470.079987 7.315713821428573
472.307007 7.327343057142857
474.53299 7.380879733333333
476.759003 7.457950150000001
478.984985 7.4930351785714295
481.210999 7.546507135714286
483.437988 7.629013013333333
485.664001 7.705600042857141
487.890015 7.796134133333334
490.115997 7.902114157142857
492.34201 8.009171571428572
494.567993 8.12765282
496.795013 8.22814397142857
499.020996 8.327679680000001
501.247009 8.45029305
503.472992 8.59598537142857
505.700012 8.742858171428574
507.925995 8.872599500000002
510.152008 9.0276004
512.377991 9.232242021428565
514.604004 9.471857571428577
516.830994 9.68935281333334
519.057007 9.928050549999996
521.28302 10.153652428571423
523.508972 10.3539466
525.734985 10.553498500000002
527.961975 10.751569642857145
530.187988 10.963014553846154
532.414001 11.158826773333336
534.640015 11.320858535714278
536.866028 11.482344457142858
539.093018 11.674551157142862
541.31897 11.858497857142856
543.544983 12.03774842142857
545.770996 12.209149685714285
547.997009 12.384050707142858
550.223999 12.558266600000003
552.450012 12.721786657142863
554.676025 12.896687678571425
556.901978 13.085841442857143
559.129028 13.255345257142856
561.35498 13.411462714285708
563.580994 13.545785285714288
565.807007 13.67887172857143
568.03302 13.811650999999996
570.26001 13.923000499999999
572.486023 14.019400985714288
574.711975 14.113999107142858
576.937988 14.196076461538464
579.164001 14.26325717142857
581.390015 14.32685757142857
583.617004 14.38103083076923
585.843018 14.423471685714286
588.06897 14.4629065
590.294983 14.501422684615385
592.520996 14.540599885714284
594.747986 14.592114085714286
596.973999 14.636323053846155
599.200012 14.702857571428574
601.426025 14.764077884615384
603.653015 14.825464821428572
605.879028 14.904965285714285
608.10498 14.991641999999997
610.330994 15.077535500000002
612.557007 15.192850349999999
614.783997 15.315292146153848
617.01001 15.439143428571423
End of preview. Expand in Data Studio

PFM-1 Landmine VNIR Hyperspectral Imaging Dataset (IGARSS 2026)

Paper Dataset License: MIT

Dataset Description

This dataset 1 contains Visible and Near-Infrared (VNIR) Hyperspectral Imaging (HSI) data prepared for the IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2026.

This specific version is a refined subset of the original benchmark dataset 2. While the original release provided full radiance cubes, broad GCP/AeroPoint data, and reference ground spectra of all the targets, this version focuses on a spatially subsetted region containing only PFM-1 landmine targets and includes high-accuracy, pixel-wise binary ground truth masks.

The data was collected over a controlled test field seeded with 143 realistic surrogate landmine and UXO targets (surface, partially buried, and fully buried). Data acquisition was performed using a Headwall Nano-Hyperspec® sensor mounted on a multi-sensor UAV platform flown at an altitude of ~20.6 m 2.

For more details regarding data acquistion and preprocessing, go to the original paper 2.

  • Sensor: [Headwall Nano-Hyperspec®]
  • Spectral Range: [398–1002 nm]
  • Number of Bands: [270 bands]
  • Approximate GSD: [Approx. 1.29 cm]

Dataset File Structure

The dataset is organized as follows:

File Name Size Description
site_with_only_mines 6.42 GB Main Hyperspectral (HSI) data cube (ENVI format), referred to as "Full Region" in the paper [1].
site_with_only_mines.hdr 23.2 kB Header file containing metadata for the HSI data cube.
roi_site_with_only_mines.roi 256 B Region of Interest (ROI) file used for spatial subsetting the original dataset from [2].
binary_ground_truth_mask_for_pfm1 5.9 MB Pixel-wise binary ground truth mask for PFM-1 mine locations.
binary_ground_truth_mask_for_pfm1.hdr 803 B Header file for the binary ground truth mask.
target_signature_pfm_1_svc.txt 7.86 kB Reference ground spectral signature for PFM-1 (captured via SVC).
.gitattributes 2.63 kB Configuration file for Git LFS and XetData tracking.

Usage

Download

pip install huggingface_hub
from huggingface_hub import snapshot_download
 
snapshot_download(repo_id="SagarLekhak/pfm1-landmine-uav-vnir-hsi-IGARSS-2026", repo_type="dataset", local_dir="./data")

To load the data using Python

import spectral.io.envi as envi

# Load the hyperspectral cube
img = envi.open('site_with_only_mines.hdr', 'site_with_only_mines')
print(f"Loaded cube with {img.shape[2]} spectral bands.")

Citation

If you use this dataset, please cite the specific IGARSS 2026 work 1 and the original benchmark paper 2:

[1] IGARSS 2026 Work:

@misc{lekhak2026benchmarkingdeeplearningstatistical,
      title={Benchmarking Deep Learning and Statistical Target Detection Methods for PFM-1 Landmine Detection in UAV Hyperspectral Imagery}, 
      author={Sagar Lekhak and Prasanna Reddy Pulakurthi and Ramesh Bhatta and Emmett J. Ientilucci},
      year={2026},
      eprint={2602.10434},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2602.10434}, 
}

[2] Original Benchmark Dataset:

@misc{lekhak2026uavbasedvnirhyperspectralbenchmark,
      title={A UAV-Based VNIR Hyperspectral Benchmark Dataset for Landmine and UXO Detection}, 
      author={Sagar Lekhak and Emmett J. Ientilucci and Jasper Baur and Susmita Ghosh},
      year={2026},
      eprint={2510.02700},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2510.02700}, 
}
Downloads last month
125

Papers for SagarLekhak/pfm1-landmine-uav-vnir-hsi-IGARSS-2026