| 한글제목(Korean Title) |
1D 통합된 근접차이에 기반한 자율적인 다중분광 영상 분할 |
| 영문제목(English Title) |
Unsupervised Multispectral Image Segmentation Based on 1D Combined Neighborhood Differences |
| 저자(Author) |
뮤잠멜
윤병춘
김덕환
Khairul Muzzammil Saipullah
Byung-Choon Yun
Deok-Hwan Kim
|
| 원문수록처(Citation) |
VOL 17 NO. 02 PP. 0625 ~ 0628 (2010. 11) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
This paper proposes a novel feature extraction method for unsupervised multispectral image segmentation based in one dimensional combined neighborhood differences (1D CND). In contrast with the original CND, which is applied with traditional image, 1D CND is computed on a single pixel with various bands. The proposed algorithm utilizes the sign of differences between bands of the pixel. The difference values are thresholded to form a binary codeword. A binomial factor is assigned to these codeword to form another unique value. These values are then grouped to construct the 1D CND feature image where is used in the unsupervised image segmentation. Various experiments using two LANDSAT multispectral images have been performed to evaluate the segmentation and classification accuracy of the proposed method. The result shows that 1D CND feature outperforms the spectral feature, with average classification accuracy of 87.55% whereas that of spectral feature is 55.81%. |
| 키워드(Keyword) |
unsupervised image segmentation
multispectral image
remote sensing
combined neighborhood differences
|
| 파일첨부 |
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