English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 111316/142225 (78%)
Visitors : 48393527      Online Users : 721
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/69887


    Title: Genetic Programming for Classification of Remote Sensing Data
    Other Titles: 遺傳程式設計法於遙測影像分類之研究
    Authors: 詹進發
    Jan, Jihn-Fa
    Keywords: 遺傳程式設計法;機器學習;影像分類
    genetic programming;machine learning;image classification
    Date: 1998.06
    Issue Date: 2014-09-12 16:23:34 (UTC+8)
    Abstract: 本研究之目的是在探討機器學習方法應用於遙測影像分類之可行性，並利用SPOT衛星資料以遺傳程式設計法進行分類，以區分植生、裸土及火災跡地。分類結果顯示遺傳程式設計法可以有效分類遙測影像，以訓練樣本進行分類之精確度可達99%，遺傳程式設計法所自動產生之電腦程式並可用於選取分類所需之重要變數。機器學習方法分類結果並與傳統之統計方法分類結果相互比較，結果顯示二者之分類效果相似。
    The overall objective of this research was to develop an adaptive machine learning technique for the classification of remote sensing data. The genetic programming paradigm was implemented to classi1 vegetation, bare soil, and burnt-over areas using SPOT multispectral data. Two SPOT imageries obtained on 31 Dec. 1986 and 15 Jan. 1988 were used in this study. The results show that the genetic programming paradigm was very effective in classi1’ing the data set (e.g., the best classification accuracy obtained was 99% for the training samples). Moreover, the computer programs derived from genetic programming allowed important variables for classification to be identified. Classification results for the machine learning approach were then compared to the results obtained using a conventional statistical approach (i.e., the Gaussian maximum likelihood classifier). The comparison shows that the classification results for both approaches are similar.
    Relation: 台灣林業科學, Vol.13, No.2, pp.109-118.
    Data Type: article
    Appears in Collections:[地政學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    9118.pdf2810KbAdobe PDF2983View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback