Detecting forest damage in cir aerial photographs using a neural network

被引:0
|
作者
Klobučar, Damir [1 ]
Pernar, Renata [2 ]
Lončarić, Sven [3 ]
Subašić, Marko [3 ]
Seletković, Ante [2 ]
Ančić, Mario [2 ]
机构
[1] Hrvatske šume d. o. o. Zagreb, Headquaters Zagreb, Ljudevita F. Vukotinovića 2, HR-10000 Zagreb, Croatia
[2] Forestry Faculty of Zagreb University, Department of Forest Management and Remote Sensing, Svetošimunska 25, HR-10000 Zagreb, Croatia
[3] University of Zagreb, Faculty of Electrical Engineering and Computing, Department of Electronic Systems and Information Processing, Unska 3, HR-10000 Zagreb, Croatia
关键词
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:157 / 163
相关论文
共 50 条
  • [41] An intelligent typhoon damage prediction system from aerial photographs
    Hsu, Chien-Chang
    Hong, Zhi-Yu
    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS: KES 2007 - WIRN 2007, PT I, PROCEEDINGS, 2007, 4692 : 747 - +
  • [42] Stratification of a forest area for multisource forest inventory by means of aerial photographs and image segmentation
    Pekkarinen, A
    Tuominen, S
    ADVANCES IN FOREST INVENTORY FOR SUSTAINABLE FOREST MANAGEMENT AND BIODIVERSITY MONITORING, 2003, 76 : 111 - 123
  • [43] DetEEktor: Mask R-CNN based neural network for energy plant identification on aerial photographs
    Schulz, Maximilian
    Boughattas, Bilel
    Wendel, Frank
    ENERGY AND AI, 2021, 5
  • [44] Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images
    Hong, Zhonghua
    Zhong, Hongzheng
    Pan, Haiyan
    Liu, Jun
    Zhou, Ruyan
    Zhang, Yun
    Han, Yanling
    Wang, Jing
    Yang, Shuhu
    Zhong, Changyue
    SENSORS, 2022, 22 (15)
  • [45] Requirements for Digital/Digitized Aerial Imagery A Manual of the Working Group of Forest Interpreters of Aerial Photographs
    Franken, Frank
    Hoffmann, Karina
    PHOTOGRAMMETRIE FERNERKUNDUNG GEOINFORMATION, 2010, (04): : 267 - 271
  • [46] Detecting DDoS attacks using adversarial neural network
    Mustapha, Ali
    Khatoun, Rida
    Zeadally, Sherali
    Chbib, Fadlallah
    Fadlallah, Ahmad
    Fahs, Walid
    El Attar, Ali
    COMPUTERS & SECURITY, 2023, 127
  • [47] Detecting distraction of drivers using Convolutional Neural Network
    Masood, Sarfaraz
    Rai, Abhinav
    Aggarwal, Aakash
    Doja, M. N.
    Ahmad, Musheer
    PATTERN RECOGNITION LETTERS, 2020, 139 : 79 - 85
  • [48] Detecting Outliers in SDSS using Convolutional Neural Network
    Sharma K.
    Kembhavi A.
    Kembhavi A.
    Sivarani T.
    Abraham S.
    Bulletin de la Societe Royale des Sciences de Liege, 2019, 88 : 174 - 181
  • [49] Assessing changes of forest area and shrub encroachment in a mire ecosystem using digital surface models and CIR aerial images
    Waser, L. T.
    Baltsavias, E.
    Ecker, K.
    Eisenbeiss, H.
    Feldmeyer-Christe, E.
    Ginzler, C.
    Kuechler, M.
    Zhang, L.
    REMOTE SENSING OF ENVIRONMENT, 2008, 112 (05) : 1956 - 1968
  • [50] Detecting Transportation Modes Using Deep Neural Network
    Wang, Hao
    Liu, GaoJun
    Duan, Jianyong
    Zhang, Lei
    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2017, E100D (05): : 1132 - 1135