一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

高級搜索

留言板

尊敬的讀者、作者、審稿人, 關(guān)于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復(fù)。謝謝您的支持!

姓名
郵箱
手機號碼
標題
留言內(nèi)容
驗證碼

基于可變類FCM算法的多光譜遙感影像分割

趙泉華,  劉曉燕,  趙雪梅,  李玉

趙泉華, 劉曉燕, 趙雪梅, 李玉. 基于可變類FCM算法的多光譜遙感影像分割[J]. 電子與信息學(xué)報, 2018, 40(1): 157-165. doi: 10.11999/JEIT170397
引用本文: 趙泉華, 劉曉燕, 趙雪梅, 李玉. 基于可變類FCM算法的多光譜遙感影像分割[J]. 電子與信息學(xué)報, 2018, 40(1): 157-165. doi: 10.11999/JEIT170397
ZHAO Quanhua, LIU Xiaoyan, ZHAO Xuemei, LI Yu. Multispectral Remote Sensing Image Segmentation Based on FCM Algorithm with Unknown Number of Clusters[J]. Journal of Electronics & Information Technology, 2018, 40(1): 157-165. doi: 10.11999/JEIT170397
Citation: ZHAO Quanhua, LIU Xiaoyan, ZHAO Xuemei, LI Yu. Multispectral Remote Sensing Image Segmentation Based on FCM Algorithm with Unknown Number of Clusters[J]. Journal of Electronics & Information Technology, 2018, 40(1): 157-165. doi: 10.11999/JEIT170397

基于可變類FCM算法的多光譜遙感影像分割

doi: 10.11999/JEIT170397

Multispectral Remote Sensing Image Segmentation Based on FCM Algorithm with Unknown Number of Clusters

  • 摘要: 為了自動確定多光譜遙感影像中地物目標類別數(shù)，該文提出一種基于可變類模糊C均值(Fuzzy C-Means, FCM)的多光譜遙感影像分割方法。首先定義像素與聚類的非相似性測度并據(jù)此構(gòu)建目標函數(shù)，而后通過求解目標函數(shù)得到最優(yōu)模糊隸屬度和聚類中心。其次，研究模糊因子與影像地物目標類別數(shù)的關(guān)系，并通過定義劃分熵(Partition Entropy, PE)指數(shù)優(yōu)選模糊因子，選擇PE指數(shù)值穩(wěn)定收斂后所對應(yīng)的最小模糊因子值為最優(yōu)模糊因子，根據(jù)模糊因子與類別數(shù)的關(guān)系得到最優(yōu)類別數(shù)，從而實現(xiàn)了影像的可變類分割。最后，利用提出算法分別對合成和真實多光譜遙感影像進行分割實驗，實驗結(jié)果表明，提出算法不僅能自動確定影像的最優(yōu)類別數(shù)，還能獲得較好的分割結(jié)果，為實現(xiàn)自動確定遙感影像中地物目標類別數(shù)提供新方法。
    Abstract: In order to automatically determine the number of clusters in multispectral remote sensing image segmentation, Fuzzy C-Means (FCM) algorithm with unknown number of clusters is proposed. First of all, a new dissimilarity measure between a pixel and a cluster is defined. The fuzzy membership function and cluster center are obtained through minimizing the objective function. Then, the relationship between fuzzy factor and the number of clusters is studied. The optimal fuzzy factor is selected by defining the Partition Entropy (PE) index and corresponding to the minimum of fuzzy factor after the convergence of PE values. According to the relationship between the fuzzy factor and the number of clusters, the optimal number of clusters is obtained, and the variable cluster segmentation of the image is realized. The analysis based on segmentation results of synthesized image and real multispectral remote sensing images show that the proposed algorithm can automatically determine the number of clusters and obtain the ideal segmentation results simultaneously. It provides a new method for automatically determine the number of clusters of remote sensing image.
    • HTML全文
  • CAO Jiazi and SONG Aiguo. Research on the texture image segmentation method based on Markov random field[J]. Chinese Journal of Scientific Instrument, 2015, 36(4): 776-786.
    曹家梓, 宋愛國. 基于馬爾科夫隨機場的紋理圖像分割方法研究[J]. 儀器儀表學(xué)報, 2015, 36(4): 776-786.
    李旭超, 朱善安. 影像分割中的馬爾可夫隨機場方法綜述[J]. 中國圖象圖形學(xué)報, 2007, 12(5): 789-797.
    LI Xuchao and ZHU Shanan. A survey of the Markov random field method for image segmentation[J]. Journal of Image and Graphics, 2007, 12(5): 789-797.
    鄭瑋, 康戈文, 陳武凡, 等. 基于馬爾可夫隨機場的無監(jiān)督遙感圖像分割算法[J]. 遙感學(xué)報, 2008, 12(2): 246-252.
    ZHENG Wei, KANG Gewen, CHEN Wufan, et al. Unsupervised segmentation of remote sensing images based on Fuzzy Markov Random Field model[J]. Journal of Remote Sensing, 2008, 12(2): 246-252.
    巫兆聰, 胡忠文, 張謙. 結(jié)合光譜、紋理與形狀結(jié)構(gòu)信息的遙感影像分割方法[J]. 測繪學(xué)報, 2013, 42(1): 44-50.
    WU Zhaocong, HU Zhongwen, and ZHANG Qian. On combining spectral, textural and shape features for remote sensing image segmentation[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(1): 44-50.
    陳海鵬, 申鉉京, 龍建武, 等. 自動確定聚類個數(shù)的模糊聚類算法[J]. 電子學(xué)報, 2017, 45(3): 687-694. doi: 10.3969/j.issn. 0372-2112. 2017.03.028.
    CHEN Haipeng, SHEN Xuanjing, LONG Jianwu, et al. Fuzzy clustering algorithm for automatic identification of clusters[J]. Acta Electronica Sinica, 2017, 45(3): 687-694. doi: 10.3969/ j.issn.0372-2112.2017.03.028.
    肖滿生, 肖哲, 文志誠, 等. 一種空間相關(guān)性與隸屬度平滑的FCM改進算法[J]. 電子與信息學(xué)報, 2017, 39(5): 1123-1129. doi: 10.11999/JEIT160710.
    XIAO Mansheng, XIAO Zhe, WEN Zhicheng, et al. Improved FCM clustering algorithm based on spatial correlation and membership smoothing[J]. Journal of Electronics Information Technology, 2017, 39(5): 1123-1129. doi: 10.11999/JEIT160710.
    周文剛, 孫挺, 朱海. 一種基于自適應(yīng)空間信息改進FCM的圖像分割算法[J]. 計算機應(yīng)用研究, 2015, 32(7): 2205-2208. doi: 10.3969/j.issn.1001-3695.2015.07.070.
    ZHOU Wengang, SUN Ting, and ZHU Hai. Image segmentation algorithm based on FCM optimized by adaptive spatial information[J]. Application Research of Computers, 2015, 32(7): 2205-2208. doi: 10.3969/j.issn.1001- 3695.2015.07.070.
    趙雪梅, 李玉, 趙泉華. 結(jié)合高斯回歸模型和隱馬爾可夫隨機場的模糊聚類圖像分割[J]. 電子與信息學(xué)報, 2014, 36(11): 2730-2736. doi: 10.3724/SP.J.1146.2013.01751.
    ZHAO Xuemei, LI Yu, and ZHAO Quanhua. Image segmentation by fuzzy clustering algorithm combining hidden Markov Random Field and Gaussian Regression model[J]. Journal of Electronics Information Technology, 2014, 36(11): 2730-2736. doi: 10.3724/SP.J.1146.2013.01751.
    沈照慶, 舒寧, 龔衍, 等. 基于改進模糊ISODATA算法的遙感影像非監(jiān)督聚類研究[J]. 遙感信息, 2008(5): 28-32.
    SHEN Zhaoqing, SHU Ning, GONG Yan, et al. Study on the supervised classification of remote sensing image based on a modified fuzzy-ISODATA algorithm[J]. Remote Sensing Information, 2008(5): 28-32.
    王建英, 孫德山, 張永. 基于馬氏距離的FCM圖像分割算法[J]. 計算機工程與應(yīng)用, 2010, 46(1): 147-149. doi: 10.3778/ j.issn.1002-8331.2010.01.045.
    WANG Jianying, SUN Deshan, and ZHANG Yong. Mahalanobis distance-based FCM image segmentation algorithm[J]. Computer Engineering and Applications, 2010, 46(1): 147-149. doi: 10.3778/j.issn.1002-8331.2010.01.045.
    BEZDEK J C. Patten Recognition with Fuzzy Objective Function Algorithms[M]. New York: Plenum Press, 1981: 79-88.
    PAL N R and BEZDEK J C. On cluster validity for the fuzzy c-means model[J]. IEEE Transactions on Fuzzy System, 1995, 3(3): 370-379.
    宮改云, 高新波, 伍忠東. FCM聚類算法中模糊加權(quán)指數(shù)m的優(yōu)選方法[J]. 模糊系統(tǒng)與數(shù)學(xué), 2005, 19(1): 143-148.
    GONG Gaiyun, GAO Xinbo, and WU Zhongdong. An optimal choice method of parameter m in FCM clustering algorithm[J]. Fuzzy Systems and Mathematics, 2005, 19(1): 143-148.
    肖滿生, 張居武. 一種基于子集測度的FCM聚類加權(quán)指數(shù)計算方法[J]. 模糊系統(tǒng)與數(shù)學(xué), 2013, 27(2): 136-141. doi: 10.3969/j.issn.1001-7402.2013.02.022.
    XIAO Mansheng and ZHANG Juwu. The weighted exponent calculation method of FCM clustering based on subset measuring[J]. Fuzzy Systems and Mathematics, 2013, 27(2): 136-141. doi: 10.3969/j.issn.1001-7402.2013.02.022.
    劉永利, 付麗麗. FCCM 算法中基于劃分熵的參數(shù)優(yōu)選方法[J]. 河南理工大學(xué)學(xué)報, 2016, 35(2): 248-253. doi: 10.16186/ j.cnki.2016.02.019.
    LIU Yongli and FU Lili. A method of parameter optimization based on partition entropy in FCCM algorithm[J]. Journal of Henan Polytechnic University, 2016, 35(2): 248-253. doi: 10.16186/j.cnki.2016.02.019.
    趙雪梅, 李玉, 趙泉華. 參數(shù)自適應(yīng)的可變類FLICM灰度圖像分割算法[J]. 控制與決策, 2017, 32(2): 262-268. doi: 10.13195/ j.kzyjc.2016.0050.
    ZHAO Xuemei, LI Yu, and ZHAO Quanhua. Self-adaptive FLICM algorithm for gray image segmentation with unknown number of clusters[J]. Control and Decision, 2017, 32(2): 262-268. doi: 10.13195/j.kzyjc.2016.0050.
    王玉, 李玉, 趙泉華. 可變類多光譜遙感圖像分割[J]. 遙感學(xué)報, 2016, 20(6): 1381-1390. doi: 10.11834/jrs.20165076.
    WANG Yu, LI Yu, and ZHAO Quanhua. Integration of multispectral remote sensing image segmentation with unknown number of classes[J]. Journal of Remote Sensing, 2016, 20(6): 1381-1390. doi: 10.11834/jrs.20165076.
  • 加載中
計量
  • 文章訪問數(shù):  1390
  • HTML全文瀏覽量:  163
  • PDF下載量:  236
  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2017-05-02
  • 修回日期:  2017-09-20
  • 刊出日期:  2018-01-19

目錄

    /

    返回文章
    返回