A UAV-Based VNIR Hyperspectral Benchmark Dataset for Landmine and UXO Detection
Abstract
This paper introduces a novel benchmark dataset of Visible and Near-Infrared (VNIR) hyperspectral imagery acquired via an unmanned aerial vehicle (UAV) platform for landmine and unexploded ordnance (UXO) detection research. The dataset was collected over a controlled test field seeded with 143 realistic surrogate landmine and UXO targets, including surface, partially buried, and fully buried configurations. Data acquisition was performed using a Headwall Nano-Hyperspec sensor mounted on a multi-sensor drone platform, flown at an altitude of approximately 20.6 m, capturing 270 contiguous spectral bands spanning 398-1002 nm. Radiometric calibration, orthorectification, and mosaicking were performed followed by reflectance retrieval using a two-point Empirical Line Method (ELM), with reference spectra acquired using an SVC spectroradiometer. Cross-validation against six reference objects yielded RMSE values below 1.0 and SAM values between 1 and 6 degrees in the 400-900 nm range, demonstrating high spectral fidelity. The dataset is released alongside raw radiance cubes, GCP/AeroPoint data, and reference spectra to support reproducible research. This contribution fills a critical gap in open-access UAV-based hyperspectral data for landmine detection and offers a multi-sensor benchmark when combined with previously published drone-based electromagnetic induction (EMI) data from the same test field.
Cite
@article{arxiv.2510.02700,
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},
journal= {arXiv preprint arXiv:2510.02700},
year = {2026}
}
Comments
This work was accepted and presented as an oral paper at the Indian Geoscience and Remote Sensing Symposium (InGARSS) 2025 and appears in the IEEE InGARSS 2025 Proceedings