Four-band Thermal Mosaicking: A New Method to Process Infrared Thermal Imagery of Urban Landscapes from UAV Flights

被引:24
|
作者
Yang, Yichen [1 ,2 ]
Lee, Xuhui [1 ,2 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Yale NUIST Ctr Atmospher Environm, Nanjing 210044, Jiangsu, Peoples R China
[2] Yale Univ, Yale Sch Forestry & Environm Studies, 195 Prospect St, New Haven, CT 06511 USA
关键词
unmanned aerial vehicle; structure-from-motion; four-band thermal mosaicking; positional error; object-based calibration; transect analysis; cluster analysis; FROM-MOTION PHOTOGRAMMETRY; VEGETATION; TEMPERATURE; VEHICLE;
D O I
10.3390/rs11111365
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Unmanned aerial vehicles (UAVs) support a large array of technological applications and scientific studies due to their ability to collect high-resolution image data. The processing of UAV data requires the use of mosaicking technology, such as structure-from-motion, which combines multiple photos to form a single image mosaic and to construct a 3-D digital model of the measurement target. However, the mosaicking of thermal images is challenging due to low lens resolution and weak contrast in the single thermal band. In this study, a novel method, referred to as four-band thermal mosaicking (FTM), was developed in order to process thermal images. The method stacks the thermal band obtained by a thermal camera onto the RGB bands acquired on the same flight by an RGB camera and mosaics the four bands simultaneously. An object-based calibration method is then used to eliminate inter-band positional errors. A UAV flight over a natural park was carried out in order to test the method. The results demonstrated that with the assistance of the high-resolution RGB bands, the method enabled successful and efficient thermal mosaicking. Transect analysis revealed an inter-band accuracy of 0.39 m or 0.68 times the ground pixel size of the thermal camera. A cluster analysis validated that the thermal mosaic captured the expected contrast of thermal properties between different surfaces within the scene.
引用
收藏
页数:17
相关论文
共 26 条
  • [1] Row and Water Front Detection from UAV Thermal-infrared Imagery for Furrow Irrigation Monitoring
    Long, Derek
    McCarthy, Cheryl
    Jensen, Troy
    2016 IEEE INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM), 2016, : 300 - 305
  • [2] A new sea ice concentration retrieval algorithm from thermal infrared imagery
    Ye, Yufang
    Wang, Xin
    Sun, Shaozhe
    Liu, Qiang
    Li, Xinqing
    Cheng, Xiao
    Chen, Zhuoqi
    INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2024, 17 (01)
  • [3] TS2uRF: A New Method for Sharpening Thermal Infrared Satellite Imagery
    Lillo-Saavedra, Mario
    Garcia-Pedrero, Angel
    Merino, Gabriel
    Gonzalo-Martin, Consuelo
    REMOTE SENSING, 2018, 10 (02)
  • [4] A Simulation Method for Thermal Infrared Imagery from Moon-Based Earth Observations
    Liao, Jingjuan
    Yuan, Linan
    Nie, Chenwei
    IEEE SENSORS JOURNAL, 2021, 21 (06) : 7736 - 7747
  • [5] An Analytic Solution Method for Retrieving Land Surface Temperature from Remotely Sensed Thermal Infrared Imagery
    Zhao, Hongrui
    Ren, Hui
    Fu, Gang
    JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2015, 43 (02) : 279 - 286
  • [6] An Analytic Solution Method for Retrieving Land Surface Temperature from Remotely Sensed Thermal Infrared Imagery
    Hongrui Zhao
    Hui Ren
    Gang Fu
    Journal of the Indian Society of Remote Sensing, 2015, 43 : 279 - 286
  • [7] Revisiting the Advanced Thermal Physical Model: New Perspectives on Thermophysical Characteristics of (341843) 2008 EV5 from Four-band WISE Data with the Sunlight-reflection Model
    Jiang, Haoxuan
    Yu, Liangliang
    Ji, Jianghui
    ASTRONOMICAL JOURNAL, 2019, 158 (05):
  • [8] Exploring a new method for the retrieval of urban thermophysical properties using thermal infrared remote sensing and deterministic modeling
    De Ridder, K.
    Bertrand, C.
    Casanova, G.
    Lefebvre, W.
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2012, 117
  • [9] Development and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery
    Masouleh, Mehdi Khoshboresh
    Shah-Hosseini, Reza
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2019, 155 : 172 - 186
  • [10] A New Algorithm for Daily Sea Ice Lead Identification in the Arctic and Antarctic Winter from Thermal-Infrared Satellite Imagery
    Reiser, Fabian
    Willmes, Sascha
    Heinemann, Guenther
    REMOTE SENSING, 2020, 12 (12)