A technique for the selection of kernel-function parameters in RBF neural networks for classification of remote-sensing images

被引：110
|
作者：
Bruzzone, L [1 ]
Prieto, DF [1 ]
机构：
[1] Univ Genoa, Dept Biophys & Elect Engn, I-16145 Genoa, Italy
来源：
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 1999年 / 37卷 / 02期
关键词：
image analysis; neural networks; pattern analysis; remote sensing;
D O I：
10.1109/36.752239
中图分类号：
P3 [地球物理学]; P59 [地球化学];
学科分类号：
0708 ; 070902 ;
摘要：
In this paper, a supervised technique for training radial basis function (RBF) neural network classifiers is proposed. Such a technique, unlike traditional ones, considers the class memberships of training samples to select the centers and widths of the kernel functions associated with the hidden neurons of an RBF network. The result is twofold: a significant reduction in the overall classification error made by the classifier and a more stable behavior of the classification error versus variations in both the number of hidden units and the initial parameters of the training process.
引用
收藏
页码：1179 / 1184
页数：6
相关论文
共 50 条
  • [21] Remote Sensing Images Recognition by Deep Convolutional Neural Networks
    Zhou, Tao
    Chen, Yuanyuan
    2018 3RD INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION ENGINEERING (ICRAE), 2018, : 202 - 205
  • [22] A new search algorithm for feature selection in high-dimensional remote-sensing images
    Bruzzone, L
    Serpico, SB
    IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING IV, 1998, 3500 : 34 - 41
  • [23] PRECISE CLASSIFICATION OF FOREST SPECIES BASED ON MULTI-SOURCE REMOTE-SENSING IMAGES
    Zhang, R.
    Li, Q.
    Duan, K. F.
    You, S. C.
    Zhang, T.
    Liu, K.
    Gan, Y. H.
    APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH, 2020, 18 (02): : 3659 - 3681
  • [24] Siamese Convolutional Neural Networks for Remote Sensing Scene Classification
    Liu, Xuning
    Zhou, Yong
    Zhao, Jiaqi
    Yao, Rui
    Liu, Bing
    Zheng, Yi
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2019, 16 (08) : 1200 - 1204
  • [25] A correction technique for false topographic perception of remote-sensing images based on an inverse topographic correction technique
    Zhang, Donghai
    Zhu, Wenquan
    Chen, Xiang
    Zheng, Zhoutao
    Zhou, Xiafei
    Jiang, Tao
    INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2016, 9 (10) : 1021 - 1034
  • [26] Training Small Networks for Scene Classification of Remote Sensing Images via Knowledge Distillation
    Chen, Guanzhou
    Zhang, Xiaodong
    Tan, Xiaoliang
    Cheng, Yufeng
    Dai, Fan
    Zhu, Kun
    Gong, Yuanfu
    Wang, Qing
    REMOTE SENSING, 2018, 10 (05)
  • [27] CLASSIFICATION OF MULTISPECTRAL REMOTE-SENSING DATA USING A BACK-PROPAGATION NEURAL NETWORK
    HEERMANN, PD
    KHAZENIE, N
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1992, 30 (01): : 81 - 88
  • [28] A Lightweight Intrinsic Mean for Remote Sensing Classification With Lie Group Kernel Function
    Xu, Chengjun
    Zhu, Guobin
    Shu, Jingqian
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2021, 18 (10) : 1741 - 1745
  • [29] Convolutional Neural Networks for Semantic Segmentation of Multispectral Remote Sensing Images
    Lopez, Josue
    Santos, Stewart
    Atzberger, Clement
    Torres, Deni
    2018 IEEE 10TH LATIN-AMERICAN CONFERENCE ON COMMUNICATIONS (IEEE LATINCOM), 2018,
  • [30] An Explainable Attention Network for Fine-Grained Ship Classification Using Remote-Sensing Images
    Xiong, Wei
    Xiong, Zhenyu
    Cui, Yaqi
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
12345