學術產出-Periodical Articles

Article View/Open

Publication Export

Google ScholarTM

政大圖書館

Citation Infomation

  • No doi shows Citation Infomation
題名 Genetic Programming for Classification of Remote Sensing Data
其他題名 遺傳程式設計法於遙測影像分類之研究
作者 詹進發
Jan, Jihn-Fa
關鍵詞 遺傳程式設計法 ; 機器學習 ; 影像分類
genetic programming ; machine learning ; image classification
日期 1998.06
上傳時間 12-Sep-2014 16:23:34 (UTC+8)
摘要 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.
關聯 台灣林業科學, Vol.13, No.2, pp.109-118.
資料類型 article
dc.creator (作者) 詹進發zh_TW
dc.creator (作者) Jan, Jihn-Faen_US
dc.date (日期) 1998.06en_US
dc.date.accessioned 12-Sep-2014 16:23:34 (UTC+8)-
dc.date.available 12-Sep-2014 16:23:34 (UTC+8)-
dc.date.issued (上傳時間) 12-Sep-2014 16:23:34 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/69887-
dc.description.abstract (摘要) 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。en_US
dc.description.abstract (摘要) 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.en_US
dc.format.extent 2878353 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) 台灣林業科學, Vol.13, No.2, pp.109-118.en_US
dc.subject (關鍵詞) 遺傳程式設計法 ; 機器學習 ; 影像分類en_US
dc.subject (關鍵詞) genetic programming ; machine learning ; image classificationen_US
dc.title (題名) Genetic Programming for Classification of Remote Sensing Dataen_US
dc.title.alternative (其他題名) 遺傳程式設計法於遙測影像分類之研究en_US
dc.type (資料類型) articleen