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

• URL: http://arxiv.org/abs/2510.02700v1
• Date: Fri, 03 Oct 2025 03:40:52 GMT
• Title: A UAV-Based VNIR Hyperspectral Benchmark Dataset for Landmine and UXO Detection
• Authors: Sagar Lekhak, Emmett J. Ientilucci, Jasper Baur, Susmita Ghosh,
• Abstract summary: 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.<n>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.
• Score: 1.3999481573773072
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• AMLID: An Adaptive Multispectral Landmine Identification Dataset for Drone-Based Detection [0.0]
  We present the first open-source dataset combining Red-Green-Blue (RGB) and Long-Wave Infrared (LWIR) imagery for Unmanned Aerial Systems (UAS)-based landmine detection.<n>AMLID comprises of 12,078 labeled images featuring 21 globally deployed landmine types across anti-personnel and anti-tank categories in both metal and plastic compositions.
  arXiv  Detail & Related papers  (2025-12-21T13:58:35Z)
• MineInsight: A Multi-sensor Dataset for Humanitarian Demining Robotics in Off-Road Environments [0.5339846068056558]
  We introduce MineInsight, a publicly available multi-sensor, multi-spectral dataset for landmine detection.<n>The dataset features 35 different targets distributed along three distinct tracks, providing a diverse and realistic testing environment.<n>MineInsight serves as a benchmark for developing and evaluating landmine detection algorithms.
  arXiv  Detail & Related papers  (2025-06-05T10:08:24Z)
• Superpowering Open-Vocabulary Object Detectors for X-ray Vision [53.07098133237041]
  Open-vocabulary object detection (OvOD) is set to revolutionize security screening by enabling systems to recognize any item in X-ray scans.<n>We propose RAXO, a framework that repurposes off-the-shelf RGB OvOD detectors for robust X-ray detection.<n> RAXO builds high-quality X-ray class descriptors using a dual-source retrieval strategy.
  arXiv  Detail & Related papers  (2025-03-21T11:54:16Z)
• Soil analysis with machine-learning-based processing of stepped-frequency GPR field measurements: Preliminary study [0.0]
  Ground Penetrating Radar (GPR) has been widely studied as a tool for extracting soil parameters relevant to agriculture and horticulture.<n>We conduct an extensive field survey using a tractor-mounted air-coupled SFCW GPR instrument.<n>We evaluate the GPR instrument's ability by predicting the apparent electrical conductivity (ECaR) measured by a co-recorded Electromagnetic Induction (EMI) instrument.
  arXiv  Detail & Related papers  (2024-04-24T16:30:12Z)
• SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection [79.23689506129733]
  We establish a new benchmark dataset and an open-source method for large-scale SAR object detection.<n>Our dataset, SARDet-100K, is a result of intense surveying, collecting, and standardizing 10 existing SAR detection datasets.<n>To the best of our knowledge, SARDet-100K is the first COCO-level large-scale multi-class SAR object detection dataset ever created.
  arXiv  Detail & Related papers  (2024-03-11T09:20:40Z)
• An Empirical Analysis of Range for 3D Object Detection [70.54345282696138]
  We present an empirical analysis of far-field 3D detection using the long-range detection dataset Argoverse 2.0. Near-field LiDAR measurements are dense and optimally encoded by small voxels, while far-field measurements are sparse and are better encoded with large voxels. We propose simple techniques to efficiently ensemble models for long-range detection that improve efficiency by 33% and boost accuracy by 3.2% CDS.
  arXiv  Detail & Related papers  (2023-08-08T05:29:26Z)
• Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration [55.41644538483948]
  This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format. The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
  arXiv  Detail & Related papers  (2023-04-27T20:21:18Z)
• The LuViRA Dataset: Synchronized Vision, Radio, and Audio Sensors for Indoor Localization [41.58739817444644]
  The dataset includes color images, corresponding depth maps, inertial measurement unit (IMU) readings, channel response between a 5G massive multiple-input and multiple-output (MIMO) testbed and user equipment. We synchronize these sensors to ensure that all data is recorded simultaneously. The main aim of this dataset is to enable research on sensor fusion with the most commonly used sensors for localization tasks.
  arXiv  Detail & Related papers  (2023-02-10T15:12:40Z)
• Supervised classification methods applied to airborne hyperspectral images: Comparative study using mutual information [0.0]
  This paper investigates the performance of four supervised learning algorithms, namely, Support Vector Machines SVM, Random Forest RF, K-Nearest Neighbors KNN and Linear Discriminant Analysis LDA. The experiments have been performed on three real hyperspectral datasets taken from the NASA's Airborne Visible/Infrared Imaging Spectrometer Sensor AVIRIS and the Reflective Optics System Imaging Spectrometer ROSIS sensors.
  arXiv  Detail & Related papers  (2022-10-27T13:39:08Z)
• Boosting 3D Object Detection by Simulating Multimodality on Point Clouds [51.87740119160152]
  This paper presents a new approach to boost a single-modality (LiDAR) 3D object detector by teaching it to simulate features and responses that follow a multi-modality (LiDAR-image) detector. The approach needs LiDAR-image data only when training the single-modality detector, and once well-trained, it only needs LiDAR data at inference. Experimental results on the nuScenes dataset show that our approach outperforms all SOTA LiDAR-only 3D detectors.
  arXiv  Detail & Related papers  (2022-06-30T01:44:30Z)
• Deep Learning based Multi-Modal Sensing for Tracking and State Extraction of Small Quadcopters [3.019035926889528]
  This paper proposes a multi-sensor based approach to detect, track, and localize a quadcopter unmanned aerial vehicle (UAV) Specifically, a pipeline is developed to process monocular RGB and thermal video (captured from a fixed platform) to detect and track the UAV in our FoV. A 2D planar lidar is used to allow conversion of pixel data to actual distance measurements, and thereby enable localization of the UAV in global coordinates.
  arXiv  Detail & Related papers  (2020-12-08T23:59:48Z)
• Depth Estimation from Monocular Images and Sparse Radar Data [93.70524512061318]
  In this paper, we explore the possibility of achieving a more accurate depth estimation by fusing monocular images and Radar points using a deep neural network. We find that the noise existing in Radar measurements is one of the main key reasons that prevents one from applying the existing fusion methods. The experiments are conducted on the nuScenes dataset, which is one of the first datasets which features Camera, Radar, and LiDAR recordings in diverse scenes and weather conditions.
  arXiv  Detail & Related papers  (2020-09-30T19:01:33Z)
• Machine Learning Techniques to Detect and Characterise Whistler Radio Waves [0.0]
  VLF antenna receivers can be used to detect whistler waves generated by lightning strokes. The identification and characterisation of whistlers are important tasks to monitor the plasmasphere in real-time. The aim of this work is to develop a machine learning-based model capable of automatically detecting whistlers in the data provided by the VLF receivers.
  arXiv  Detail & Related papers  (2020-02-04T12:05:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.