Monitoring crop cycles by SAR using a neural network trained by a model

被引:0
|
作者
Del Frate, F [1 ]
Ferrazzoli, P [1 ]
Guerriero, L [1 ]
Strozzi, T [1 ]
Wegmüller, U [1 ]
Cookmartin, G [1 ]
Quegan, S [1 ]
机构
[1] Univ Tor Vergata Ingn, DISP, I-00133 Rome, Italy
关键词
D O I
暂无
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
An algorithm, based on an electromagnetic model and a neural network, aimed at monitoring the multitemporal evolution of wheat fields, is described. Three different sites are used to validate the model, provide reference ground data, and test the algorithm.
引用
收藏
页码:239 / 244
页数:6
相关论文
共 50 条
  • [41] Rice crop monitoring using X, C and L band SAR data
    Suga, Yuzo
    Konishi, Tomohisa
    32nd Asian Conference on Remote Sensing 2011, ACRS 2011, 2011, 3 : 1553 - 1558
  • [42] Classification of Pistachio Varieties Using Pre-trained Architectures and a Proposed Convolutional Neural Network Model
    Idress, Khaled Adil Dawood
    Oztekin, Yesim Benal
    Gadalla, Omsalma Alsadig Adam
    Baitu, Geofrey Prudence
    15TH INTERNATIONAL CONGRESS ON AGRICULTURAL MECHANIZATION AND ENERGY IN AGRICULTURE, ANKAGENG 2023, 2024, 458 : 148 - 163
  • [43] A Radar Vegetation Index for Crop Monitoring Using Compact Polarimetric SAR Data
    Mandal, Dipankar
    Ratha, Debanshu
    Bhattacharya, Avik
    Kumar, Vineet
    McNairn, Heather
    Rao, Yalamanchili S.
    Frery, Alejandro C.
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 58 (09): : 6321 - 6335
  • [44] A novel classification model for cotton yarn quality based on trained neural network using genetic algorithm
    Amin, A. E.
    KNOWLEDGE-BASED SYSTEMS, 2013, 39 : 124 - 132
  • [45] The Application of an Artificial Neural Network as a Baseline Model for Condition Monitoring of Innovative Humidified Micro Gas Turbine Cycles
    Colquhoun, Kathryn
    Somehsaraei, Homam Nikpey
    De Paepe, Ward
    JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME, 2024, 146 (05):
  • [46] THE APPLICATION OF AN ARTIFICIAL NEURAL NETWORK AS A BASELINE MODEL FOR CONDITION MONITORING OF INNOVATIVE HUMIDIFIED MICRO GAS TURBINE CYCLES
    Colquhoun, Kathryn
    Somehsaraei, Homam Nikpey
    De Paepe, Ward
    PROCEEDINGS OF ASME TURBO EXPO 2022: TURBOMACHINERY TECHNICAL CONFERENCE AND EXPOSITION, GT2022, VOL 4, 2022,
  • [47] Process monitoring of abrasive flow machining using a neural network predictive model
    Lam, SSY
    Smith, AE
    6TH INDUSTRIAL ENGINEERING RESEARCH CONFERENCE PROCEEDINGS: (IERC), 1997, : 477 - 482
  • [48] Monitoring the quality of ground water in pipelines using deep neural network model
    Kumar, M. Ashok
    Srinivas, N.
    Ramya, P.
    Ahlawat, Neha
    Sharma, Jaya
    Vinod, Franklin
    GROUNDWATER FOR SUSTAINABLE DEVELOPMENT, 2024, 24
  • [49] Development of Convolutional Neural Network Model for Crop Yield Prediction
    Ghildiyal, Shivangi
    Deogaonkar, Anant
    Bhandari, Narendra Singh
    Bisht, Mamta
    Vichoray, Chandan
    Naval, Naveen
    2024 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT CYBER PHYSICAL SYSTEMS AND INTERNET OF THINGS, ICOICI 2024, 2024, : 1130 - 1135
  • [50] A Convolutional Neural Network Model for Wheat Crop Disease Prediction
    Ashraf, Mahmood
    Abrar, Mohammad
    Qadeer, Nauman
    Alshdadi, Abdulrahman A.
    Sabbah, Thabit
    Khan, Muhammad Attique
    CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 75 (02): : 3867 - 3882