A UAV-Based VNIR Hyperspectral Benchmark Dataset for Landmine and UXO Detection

2025-10-03 · Sagar Lekhak, Emmett J. Ientilucci, Jasper Baur, Susmita Ghosh arxiv

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.

📄 PDF Abstract BibTeX arXiv:2510.02700

Code (0)

등록된 구현이 없습니다.

Human-in-the-Loop Signature Bootstrapping for UAV Hyperspectral PFM-1 Mine Detection

2026-07-28 · Sagar Lekhak, Prasanna Reddy Pulakurthi, Emmett J. Ientilucci arxiv

Hyperspectral imaging (HSI) is useful for material discrimination, but operational mine screening also depends on how many false alarms must be inspected before targets are found. This paper studies PFM-1 landmine detect…

Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data

2018-04-24 · Sina Keller, Felix M. Riese, Johanna Stötzer, Philipp M. Maier 외

In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with LWIR data. Measurements from a multi-sensor field campaign represent the benchmark datase…

BIG-bench Machine Learning feature selection regression

Calibrated Vehicle Paint Signatures for Simulating Hyperspectral Imagery

2020-04-16

We investigate a procedure for rapidly adding calibrated vehicle visible-near infrared (VNIR) paint signatures to an existing hyperspectral simulator - The Digital Imaging and Remote Sensing Image Generation (DIRSIG) mod…

Diversity Image Generation

AMLID: An Adaptive Multispectral Landmine Identification Dataset for Drone-Based Detection

2025-12-21 · James E. Gallagher, Edward J. Oughton arxiv

Landmines remain a persistent humanitarian threat, with an estimated 110 million mines deployed across 60 countries, claiming approximately 26,000 casualties annually. Current detection methods are hazardous, inefficient…

Comparing Surface Landmine Object Detection Models on a New Drone Flyby Dataset

2024-10-17 · Navin Agrawal-Chung, Zohran Moin

Landmine detection using traditional methods is slow, dangerous and prohibitively expensive. Using deep learning-based object detection algorithms drone videos is promising but has multiple challenges due to the small, s…

Landmine object-detection Object Detection