TWO-DIMENSIONAL NEURAL NETWORK ENTROPY FOR REMOTE SENSING IMAGE ANALYSIS

被引：0
|
作者：
Velichko, Andrei [1 ]
Wagner, Matthias P. [2 ]
Taravat, Alireza [3 ]
机构：
[1] Petrozavodsk State Univ, Inst Phys & Technol, Petrozavodsk, Russia
[2] Panopterra, Darmstadt, Germany
[3] Deimos Space, Oxford OX110QR, England
来源：
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) | 2022年
关键词：
Entropy; image features; sentinel-2; remote sensing;
D O I：
10.1109/IGARSS46834.2022.9883430
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
Measuring the predictability and complexity of time series using entropy is an essential tool for designing and controlling a nonlinear system in the remote sensing field. However, the existing methods have some drawbacks related to their strong dependence on method parameters. To overcome these difficulties, this study proposes a new method for estimating the two-dimensional neural network entropy (NNetEn2D) for evaluating the regularity or predictability of images using the LogNNet neural network model.
引用
收藏
页码：1952 / 1954
页数：3
相关论文
共 50 条
  • [1] NNetEn2D: Two-Dimensional Neural Network Entropy in Remote Sensing Imagery and Geophysical Mapping
    Velichko, Andrei
    Wagner, Matthias P.
    Taravat, Alireza
    Hobbs, Bruce
    Ord, Alison
    REMOTE SENSING, 2022, 14 (09)
  • [2] An Automatic Exposure Method of Plane Array Remote Sensing Image Based on Two-Dimensional Entropy
    Gao, Tan
    Zheng, Liangliang
    Xu, Wei
    Piao, Yongjie
    Feng, Rupeng
    Chen, Xiaolong
    Zhou, Tichao
    SENSORS, 2021, 21 (10)
  • [3] Two-Dimensional Fast Orthogonal Neural Network for Image Recognition
    Stasiak, Bartlomiej
    PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, PROCEEDINGS, 2009, 5856 : 653 - 660
  • [4] Remote sensing images segmentation of rivers based on two-dimensional reciprocal gray entropy
    Wu, Yiquan
    Meng, Tianliang
    Wu, Shihua
    Lu, Wenping
    Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2014, 42 (12): : 70 - 74
  • [5] Comparison with two classification algorithms of remote sensing image based on neural network
    Chen, YM
    Wan, YC
    Gong, JY
    Chen, J
    ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1, 2004, 3173 : 906 - 911
  • [6] Two new neural network approaches to two-dimensional CT image reconstruction
    Ali, FEF
    Nakao, Z
    Chen, YW
    FUZZY SETS AND SYSTEMS, 1999, 103 (02) : 295 - 302
  • [7] A method of invariant neural-network two-dimensional image recognition
    Sosulin, YG
    Pham, TD
    JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS, 2004, 49 (05) : 557 - 562
  • [8] Two-dimensional multiscale entropy analysis: Applications to image texture evaluation
    Silva, Luiz E. V.
    Duque, Juliano J.
    Felipe, Joaquim C.
    Murta, Luiz O., Jr.
    Humeau-Heurtier, Anne
    SIGNAL PROCESSING, 2018, 147 : 224 - 232
  • [9] Remote Sensing Image Fusion with Convolutional Neural Network
    Zhong J.
    Yang B.
    Huang G.
    Zhong F.
    Chen Z.
    Sensing and Imaging, 2016, 17 (1):
  • [10] A Novel Neural Network for Remote Sensing Image Matching
    Zhu, Hao
    Jiao, Licheng
    Ma, Wenping
    Liu, Fang
    Zhao, Wei
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 30 (09) : 2853 - 2865
12345