HYPERTHUN'22: A MULTI-SENSOR MULTI-TEMPORAL CAMOUFLAGE DETECTION CAMPAIGN

被引:0
|
作者
Vogtli, M. [1 ]
Sierro, L. [1 ]
Kneubuhler, M. [1 ]
Schreiner, S. [2 ]
Gross, W. [2 ]
Queck, F. [2 ]
Kuester, J. [2 ]
Mispelhorn, J. [2 ]
Middelmann, W. [2 ]
机构
[1] Univ Zurich, Remote Sensing Labs RSL, Winterthurerstr 190, CH-8057 Zurich, Switzerland
[2] Fraunhofer IOSB, Image Anal Grp, Gutleuthausstr 1, DE-76275 Ettlingen, Germany
关键词
Hyperspectral; UAV; Target Detection; Camouflage;
D O I
10.1109/IGARSS52108.2023.10282104
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
HyperThun'22 was a multi-sensor and multi-temporal camouflage detection campaign with drone-carried hyperspectral, thermal, and RGB instruments. In more than 20 flights, various military targets were imaged with the purpose of analysing detection rates, camouflage transparency, and system performances. This article presents the campaign design, the data processing, and first data insights. Preliminary results show the potential of the acquired data for promising studies.
引用
收藏
页码:2153 / 2156
页数:4
相关论文
共 50 条
  • [1] A multi-temporal multi-sensor circular fusion filter
    Stienne, G.
    Reboul, S.
    Azmani, M.
    Choquel, J. B.
    Benjelloun, M.
    INFORMATION FUSION, 2014, 18 : 86 - 100
  • [2] STUBBLE BURNING DETECTION USING MULTI-SENSOR AND MULTI-TEMPORAL SATELLITE DATA
    Garg, Aseem
    Vescovi, Fabio Domenico
    Chhipa, Vaibhav
    Kumar, Ajay
    Prasad, Shubham
    Aravind, S.
    Guthula, Venkanna Babu
    Pankajakshan, Praveen
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 1606 - 1609
  • [3] Analyzing multi-sensor data fusion techniques: A multi-temporal change detection approach
    University of California, Los Angeles, CA, United States
    不详
    Am. Soc. Photogramm. Remote Sens. - ASPRS Annu. Conf.: Identifying Geospatial Solutions, 2007, (656-665):
  • [4] FUSION OF MULTI-TEMPORAL AND MULTI-SENSOR ICE VELOCITY OBSERVATIONS
    Charrier, Laurane
    Yan, Yajing
    Koeniguer, Elise Colin
    Mouginot, Jeremie
    Milian, Romain
    Trouve, Emmanuel
    XXIV ISPRS CONGRESS: IMAGING TODAY, FORESEEING TOMORROW, COMMISSION III, 2022, 5-3 : 311 - 318
  • [5] A MULTI-TEMPORAL HYPERSPECTRAL CAMOUFLAGE DETECTION AND TRANSPARENCY EXPERIMENT
    Gross, Wolfgang
    Queck, Florian
    Schreiner, Simon
    Voegtli, Marius
    Kuester, Jannick
    Mispelhorn, Jonas
    Kneubuehler, Mathias
    Middelmann, Wolfgang
    TARGET AND BACKGROUND SIGNATURES VIII, 2022, 12270
  • [6] MULTI-TEMPORAL AND MULTI-SENSOR IMAGE MATCHING BASED ON LOCAL FREQUENCY INFORMATION
    Liu, Xiaochun
    Yu, Qifeng
    Zhang, Xiaohu
    Shang, Yang
    Zhu, Xianwei
    Lei, Zhihui
    XXII ISPRS CONGRESS, TECHNICAL COMMISSION III, 2012, 39-B3 : 485 - 490
  • [7] Global Snow Cover Mapping Using a Multi-Temporal Multi-Sensor Approach
    Rudjord, Oystein
    Salberg, Arnt-Borre
    Solberg, Rune
    2015 8TH INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTITEMPORAL REMOTE SENSING IMAGES (MULTI-TEMP), 2015,
  • [8] Assessment of environmental impacts of a barrage system using multi-temporal multi-sensor data
    Kristof, D
    Ducrot, D
    NEW STRATEGIES FOR EUROPEAN REMOTE SENSING, 2005, : 539 - 545
  • [9] Fusion of Multi-temporal and Multi-sensor Hyperspectral Data for Land-Use Classification
    Piqueras-Salazar, Ignacio
    Garcia-Sevilla, Pedro
    PATTERN RECOGNITION AND IMAGE ANALYSIS, IBPRIA 2013, 2013, 7887 : 724 - 731
  • [10] Infrastructure assessment for disaster management using multi-sensor and multi-temporal remote sensing imagery
    Butenuth, Matthias
    Frey, Daniel
    Nielsen, Allan Aasbjerg
    Skriver, Henning
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2011, 32 (23) : 8575 - 8594