Recognition of Urbanized Areas in UAV-Derived Very-High-Resolution Visible-Light Imagery

被引:0
|
作者
Puniach, Edyta [1 ]
Gruszczynski, Wojciech [1 ]
Cwiakala, Pawel [1 ]
Strzabala, Katarzyna [1 ]
Pastucha, Elzbieta [2 ]
机构
[1] AGH Univ Krakow, Fac Geodata Sci Geodesy & Environm Engn, Mickiewicza 30, PL-30059 Krakow, Poland
[2] Univ Southern Denmark, Maersk Mc Kinney Moller Inst, Campusvej 55, DK-5230 Odense, Denmark
关键词
image classification; neural networks; unmanned aerial vehicle (UAV); vegetation indices; visible-light imagery; VEGETATION INDEXES; CLASSIFICATION;
D O I
10.3390/rs16183444
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study compared classifiers that differentiate between urbanized and non-urbanized areas based on unmanned aerial vehicle (UAV)-acquired RGB imagery. The tested solutions included numerous vegetation indices (VIs) thresholding and neural networks (NNs). The analysis was conducted for two study areas for which surveys were carried out using different UAVs and cameras. The ground sampling distances for the study areas were 10 mm and 15 mm, respectively. Reference classification was performed manually, obtaining approximately 24 million classified pixels for the first area and approximately 3.8 million for the second. This research study included an analysis of the impact of the season on the threshold values for the tested VIs and the impact of image patch size provided as inputs for the NNs on classification accuracy. The results of the conducted research study indicate a higher classification accuracy using NNs (about 96%) compared with the best of the tested VIs, i.e., Excess Blue (about 87%). Due to the highly imbalanced nature of the used datasets (non-urbanized areas constitute approximately 87% of the total datasets), the Matthews correlation coefficient was also used to assess the correctness of the classification. The analysis based on statistical measures was supplemented with a qualitative assessment of the classification results, which allowed the identification of the most important sources of differences in classification between VIs thresholding and NNs.
引用
收藏
页数:21
相关论文
共 50 条
  • [31] Shadow information recovery in urban areas from very high resolution satellite imagery
    Chen, Y.
    Wen, D.
    Jing, L.
    Shi, P.
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2007, 28 (15) : 3249 - 3254
  • [32] Effects of the Construction of Granadilla Industrial Port in Seagrass and Seaweed Habitats Using Very-High-Resolution Multispectral Satellite Imagery
    Mederos-Barrera, Antonio
    Sevilla, Jose
    Marcello, Javier
    Espinosa, Jose Maria
    Eugenio, Francisco
    REMOTE SENSING, 2024, 16 (06)
  • [33] COUNTRY-SCALE PHOTOGRAMMETRY AND TRUE ORTHORECTIFICATION USING VERY-HIGH-RESOLUTION (VHR) MULTI-VIEW SATELLITE IMAGERY
    Maxwell, Nicholas
    Papadakis, John
    Ilie, Adrian
    Stutts, S. Craig
    Bates, Joseph
    Raskob, Benjamin L.
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 5607 - 5610
  • [34] Deep learning-based individual tree crown delineation in mangrove forests using very-high-resolution satellite imagery
    Lassalle, Guillaume
    Ferreira, Matheus Pinheiro
    La Rosa, Laura Elena Cué
    de Souza Filho, Carlos Roberto
    ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 189 : 220 - 235
  • [35] Genetic Particle Swarm Optimization Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection
    Chen, Qiang
    Chen, Yunhao
    Jiang, Weiguo
    SENSORS, 2016, 16 (08)
  • [36] Exploring the Relationship Between Very-High-Resolution Satellite Imagery Data and Fruit Count for Predicting Mango Yield at Multiple Scales
    Torgbor, Benjamin Adjah
    Sinha, Priyakant
    Rahman, Muhammad Moshiur
    Robson, Andrew
    Brinkhoff, James
    Suarez, Luz Angelica
    REMOTE SENSING, 2024, 16 (22)
  • [37] Deep learning-based individual tree crown delineation in mangrove forests using very-high-resolution satellite imagery
    Lassalle, Guillaume
    Ferreira, Matheus Pinheiro
    La Rosa, Laura Elena Cue
    de Souza Filho, Carlos Roberto
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2022, 189 : 220 - 235
  • [38] A graph-based approach for the co-registration refinement of very-high-resolution imagery and digital line graphic data
    Guo, Zhou
    Du, Shihong
    Zhao, Wenzhi
    He, Fangning
    Lin, Yun-Jou
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2016, 37 (17) : 4015 - 4034
  • [39] Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak
    Dash, Jonathan P.
    Watt, Michael S.
    Pearse, Grant D.
    Heaphy, Marie
    Dungey, Heidi S.
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2017, 131 : 1 - 14
  • [40] Automated digital elevation model (DEM) generation from very-high-resolution Planet SkySat triplet stereo and video imagery
    Bhushan, Shashank
    Shean, David
    Alexandrov, Oleg
    Henderson, Scott
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2021, 173 : 151 - 165