Some novel approaches in laser remote sensing of natural waters based on recognition of spectral patterns with the help of artificial neural networks

被引：0
|
作者：
Dolenko, T. A. [1 ]
Fadeeva, I. V. [1 ]
Burikov, S. A. [1 ]
Dolenko, S. A. [2 ]
Fadeev, V. V. [1 ]
机构：
[1] Moscow MV Lomonosov State Univ, Dept Phys, Vorobevy Gory 1-2, Moscow 119992, Russia
[2] Moscow MV Lomonosov State Univ, Nucl Phys Inst, Moscow 119992, Russia
来源：
CURRENT RESEARCH ON REMOTE SENSING, LASER PROBING, AND IMAGERY IN NATURAL WATERS | 2007年 / 6615卷
关键词：
natural waters; laser remote sensing; fluorescence; spectral patterns; artificial neural networks;
D O I：
10.1117/12.740453
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
The results of research of application of spectral patterns recognition technique in laser remote sensing of natural waters, conducted by the authors in 2001 - 2006 years, are summarized in this paper. It is shown that application of artificial neural networks allows to perform precision analysis of water Raman scattering and fluorescence bands of humic substances (with possible distortions of those by fluorescence of pollution and other organic contaminants). Based on this, it is possible to solve such problems of laser diagnostics of natural waters as remote determination of temperature, identification and determination of concentration of salts, humic substances, oil pollutions in water, monitoring of hydrological structures.
引用
收藏
页数：12
相关论文
共 38 条
  • [1] Remote sensing of water depths in shallow waters via artificial neural networks
    Ceyhun, Oezcelik
    Yalcin, Arisoy
    [J]. ESTUARINE COASTAL AND SHELF SCIENCE, 2010, 89 (01) : 89 - 96
  • [2] Novel artificial neural networks for remote-sensing data classification
    Tao, XL
    Michel, HE
    [J]. Optics and Photonics in Global Homeland Security, 2005, 5781 : 127 - 138
  • [3] Study on Remote Sensing of Water Depth Extraction Based on Artificial Neural Networks
    Zhang, Zhenxing
    Hao, Yanling
    [J]. EPLWW3S 2011: 2011 INTERNATIONAL CONFERENCE ON ECOLOGICAL PROTECTION OF LAKES-WETLANDS-WATERSHED AND APPLICATION OF 3S TECHNOLOGY, VOL 2, 2011, : 586 - 589
  • [4] Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification
    Anwer, Rao Muhammad
    Khan, Fahad Shahbaz
    van de Weijer, Joost
    Molinier, Matthieu
    Laaksonen, Jorma
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2018, 138 : 74 - 85
  • [5] Novel approaches to signal transmission based on chaotic signals and artificial neural networks
    Müller, A
    Elmirghani, JMH
    [J]. IEEE TRANSACTIONS ON COMMUNICATIONS, 2002, 50 (03) : 384 - 390
  • [6] Artificial neural networks model based on remote sensing to retrieve evapotranspiration over the Brazilian Pampa
    Kafer, Pamela Suelen
    da Rocha, Mita Souza
    Diaz, Lucas Ribeiro
    Kaiser, Eduardo Andre
    Santos, Daniel Caetano
    Veeck, Gustavo Pujol
    Roberti, Debora Regina
    Alves Rolim, Silvia Beatriz
    de Oliveira, Guilherme Garcia
    [J]. JOURNAL OF APPLIED REMOTE SENSING, 2020, 14 (03):
  • [7] A Novel Technique for Segmentation of High Resolution Remote Sensing Images Based on Neural Networks
    Mohammad Barr
    [J]. Neural Processing Letters, 2020, 52 : 679 - 692
  • [8] A Novel Technique for Segmentation of High Resolution Remote Sensing Images Based on Neural Networks
    Barr, Mohammad
    [J]. NEURAL PROCESSING LETTERS, 2020, 52 (01) : 679 - 692
  • [9] Climate change or regional human impacts? Remote sensing tools, artificial neural networks, and wavelet approaches aim to solve the problem
    Foroumandi, Ehsan
    Nourani, Vahid
    Sharghi, Elnaz
    [J]. HYDROLOGY RESEARCH, 2021, 52 (01): : 176 - 195
  • [10] Novel Feature Extraction Methodology with Evaluation in Artificial Neural Networks Based Fingerprint Recognition System
    Kahraman, Nihan
    Cam Taskiran, Zehra Gulru
    Taskiran, Murat
    [J]. TEHNICKI VJESNIK-TECHNICAL GAZETTE, 2018, 25 : 112 - 119
1234