A new method of vegetation mapping by object-based classification using high resolution satellite data
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- KAMAGATA Noritoshi
- 東京情報大学大学院
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- HARA Keitarou
- 東京情報大学
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- MORI Masaru
- 国際航業
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- AKAMATSU Yukio
- 国際航業
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- LI Yunqing
- 日本スペースイメージング
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- HOSHINO Yoshinobu
- 東京農工大学農学部
Bibliographic Information
- Other Title
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- 高分解能衛星データのオブジェクト指向分類による植生図作成手法の提案
- コウ ブンカイノウ エイセイ データ ノ オブジェクト シコウ ブンルイ ニ ヨル ショクセイズ サクセイ シュホウ ノ テイアン
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Abstract
The effectiveness of object-based classification using high resolution satellite data was examined to establish in applying to vegetation mapping. We compared object-based and pixel-based classifiers for secondary forests in a rural area in the east part of Chiba prefecture. The minimum distance classifier as the object-based classification, and the maximum likelihood classifier and the ISODATA classifier as the pixel-based classification were applied. The results showed that the overall classification accuracy and Kappa statistics of object-based classification were higher than those of pixel-based, ISODATA and maximum likelihood classifications (overall classification accuracy of object-based : 64.17%, maximum likelihood : 60.17%, ISODATA : 53.64% and Kappa statistics of object-based : 0.551, maximum likelihood : 0.497, ISODATA : 0.388, respectively) . Boundaries of each plant community were well extracted by object-based classification. This research clarified that the object-based classification method is useful and has high potentiality in vegetation mapping.
Journal
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- Journal of the Japan society of photogrammetry and remote sensing
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Journal of the Japan society of photogrammetry and remote sensing 45 (1), 43-49, 2006
Japan Society of Photogrammetry and Remote Sensing
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Details 詳細情報について
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- CRID
- 1390001204078140032
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- NII Article ID
- 10019600668
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- NII Book ID
- AN00111450
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- ISSN
- 18839061
- 02855844
- http://id.crossref.org/issn/02855844
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- NDL BIB ID
- 7889937
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed