An Implementation of a Land Cover Pattern Classification System for Remotely Sensed Data by Using Neuro-Fuzzy Algorithm
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- Lee Sang-Gu
- Hannam Univ.
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- Han Jong-Gyu
- Hannam Univ.
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- Lee Hee-Hyol
- Fukuoka Inst. of Tech.
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- Miyazaki Michio
- Kanto Gakuin Univ.
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- Akizuki Kageo
- Waseda Univ.
Bibliographic Information
- Other Title
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- ニューロ・ファジィアルゴリズムを用いた遠隔探査画像の地表面パターン分類システム
- ニューロ ファジィ アルゴリズム オ モチイタ エンカク タンサ ガゾウ ノ チヒョウメン パターン ブンルイ システム
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Abstract
In this paper, we implement a land cover pattern classification system for remotely sensed data by using a neuro-fuzzy algorithm, and compare it with the conventional methods of the Back-Propagation learning and the Maximum-Likelihood algorithm. The neuro-fuzzy pattern classifier has a 3-layer feed-forward architecture that is derived from a generic fuzzy perceptron. The digital image used in our research was acquired with the AMS (Airborne Multispectral Scanner). We determine the eight classes covered the majority of land cover feature on Daeduk Science Town. The results show that the proposed classifier is considerably more accurate to the mixed composition area with complex classes.
Journal
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- IEEJ Transactions on Electronics, Information and Systems
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IEEJ Transactions on Electronics, Information and Systems 120 (4), 546-553, 2000
The Institute of Electrical Engineers of Japan
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Keywords
Details 詳細情報について
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- CRID
- 1390001204609726720
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- NII Article ID
- 10005313850
- 130006845099
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- NII Book ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL BIB ID
- 5333770
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed