PROCEDURES FOR PROCESSING THERMAL IMAGES USING LOW-COST MICROBOLOMETER CAMERAS FOR SMALL UNMANNED AERIAL SYSTEMS

被引:17
作者
Jensen, Austin M. [1 ]
McKee, Mac [1 ]
Chen, YangQuan
机构
[1] Utah State Univ, Utah Water Res Lab, Logan, UT 84322 USA
来源
2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2014年
关键词
Thermal Infrared; TIR; Remote Sensing; Unmanned Aerial System; UAS; UAV; Calibration;
D O I
10.1109/IGARSS.2014.6947013
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Remote sensing data using thermal-infrared (TIR) cameras can be very helpful for many applications including agriculture and thermal refugia for fish habitat. Small unmanned aerial vehicles (UAS) can be efficient platforms for providing high-resolution thermal imagery at a low-cost; however most thermal cameras used for remote sensing are cooled systems and can be very large, expensive, and consume large amounts of power. Because of these constraints they are not easily integrated with small UAS. For surveillance and military applications, microbolometer thermal cameras are widely used on UAS because they are smaller, less expensive and consume less power than cooled thermal cameras. However, microbolometer thermal cameras are not as sensitive and accurate as cooled systems. Also, many are not calibrated and only measure relative temperature. This presents a challenge when microbolometer thermal cameras are used for scientific and ecological applications: absolute surface temperature is necessary for these applications. This paper presents procedures that can be followed to convert the raw images from a microbolometer camera to accurately represent surface temperature (without compensating for emissivity). The steps needed for this include preparing the images for orthoretification, geometric calibration, orthorectification, and correction for external disturbances.
引用
收藏
页码:2629 / 2632
页数:4
相关论文
共 10 条
  • [1] [Anonymous], 2012, Infrared Cameras Incorporated
  • [2] [Anonymous], 2012, MosaicMill EnsoMOSAIC
  • [3] Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle
    Berni, Jose A. J.
    Zarco-Tejada, Pablo J.
    Suarez, Lola
    Fereres, Elias
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2009, 47 (03): : 722 - 738
  • [4] GALLO MA, 1993, P SOC PHOTO-OPT INS, V2020, P351, DOI 10.1117/12.160556
  • [5] A four-step camera calibration procedure with implicit image correction
    Heikkila, J
    Silven, O
    [J]. 1997 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS, 1997, : 1106 - 1112
  • [6] Jensen A.M., 2009, THESIS UTAH STATE U
  • [7] Jensen Austin M., 2013, P IEEE INT GEOSC REM
  • [8] Uncooled infrared sensors for surveillance and law enforcement applications
    Parker, TW
    Marshall, CA
    Kohin, M
    Murphy, R
    [J]. SURVEILLANCE AND ASSESSMENT TECHNOLOGIES FOR LAW ENFORCEMENT, 1997, 2935 : 182 - 187
  • [9] Prata A. J., 1994, J GEOPHYS RES, V99
  • [10] Airborne thermal remote sensing for water temperature assessment in rivers and streams
    Torgersen, CE
    Faux, RN
    McIntosh, BA
    Poage, NJ
    Norton, DJ
    [J]. REMOTE SENSING OF ENVIRONMENT, 2001, 76 (03) : 386 - 398