Spatially resolved spectral unmixing using convolutional neural networks

被引：3
|
作者：
Anastasiadis, Johannes [1 ]
Leon, Fernando Puente [1 ]
机构：
[1] Karlsruher Inst Technol, Inst Ind Informat Tech, Karlsruhe, Germany
来源：
TM-TECHNISCHES MESSEN | 2019年 / 86卷
关键词：
Hyperspectral image; spectral unmixing; convolutional neural networks; MATERIAL ABUNDANCES; FOOD;
D O I：
10.1515/teme-2019-0062
中图分类号：
TH7 [仪器、仪表];
学科分类号：
0804 ; 080401 ; 081102 ;
摘要：
This paper proposes a model-free approach for spectral unmixing based on a convolutional neural network. It is evaluated using sample data sets and compared with methods based on the linear mixing model. Furthermore, methods to ensure physical plausibility of the estimated abundances are presented. This approach involves output layers that enforce non-negativity and the sum-to-one constraint.
引用
收藏
页码：S122 / S126
页数：5
相关论文
共 50 条
  • [1] Spatially resolved spectral unmixing using convolutional neural networks
    Anastasiadis, Johannes
    Puente León, Fernando
    Technisches Messen, 2019, 86 (s1):
  • [2] Hyperspectral Unmixing Based on Spectral and Sparse Deep Convolutional Neural Networks
    Wan, Lulu
    Chen, Tao
    Plaza, Antonio
    Cai, Haojie
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 : 11669 - 11682
  • [3] Feature Reduction in Spectral Unmixing Using Neural Networks
    Rayeni, Farshid Khajeh
    Ghassemian, Hassan
    2017 3RD IRANIAN CONFERENCE ON SIGNAL PROCESSING AND INTELLIGENT SYSTEMS (ICSPIS), 2017, : 178 - 182
  • [4] Classification of Oil Palm Diseases via Spectral Unmixing and Convolutional Neural Networks
    Pinto, Jhon E.
    Ramirez, Juan M.
    Arguello, Henry
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XXI, 2019, 11149
  • [5] Generation of artificial data sets to train convolutional neural networks for spectral unmixing
    Anastasiadis, Johannes
    Benzing, Philipp
    Leon, Fernando Puente
    TM-TECHNISCHES MESSEN, 2020, 87 (09) : 542 - 552
  • [6] Hyperspectral Unmixing via Deep Convolutional Neural Networks
    Zhang, Xiangrong
    Sun, Yujia
    Zhang, Jingyan
    Wu, Peng
    Jiao, Licheng
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2018, 15 (11) : 1755 - 1759
  • [7] Diagnosing Convolutional Neural Networks using their Spectral Response
    Stamatescu, Victor
    McDonnell, Mark D.
    2018 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA), 2018, : 603 - 610
  • [8] SPATIALLY INFORMED SPECTRAL UNMIXING
    Bongiorno, Daniel L.
    Fairley, Adam J.
    Williams, Stefan B.
    2015 7TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS), 2015,
  • [9] SPARSE UNMIXING USING DEEP CONVOLUTIONAL NETWORKS
    Rasti, Behnood
    Koirala, Bikram
    Scheunders, Paul
    2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 24 - 27
  • [10] SPARCNN: SPAtially Related Convolutional Neural Networks
    Turner, J. T.
    Gupta, Kalyan Moy
    Aha, David
    2016 IEEE APPLIED IMAGERY PATTERN RECOGNITION WORKSHOP (AIPR), 2016,
12345