Proceedings of the Korea Information Processing Society Conference (한국정보처리학회:학술대회논문집)
- 2010.11a
- /
- Pages.625-628
- /
- 2010
- /
- 2005-0011(pISSN)
- /
- 2671-7298(eISSN)
DOI QR Code
Unsupervised Multispectral Image Segmentation Based on 1D Combined Neighborhood Differences
1D 통합된 근접차이에 기반한 자율적인 다중분광 영상 분할
- Saipullah, Khairul Muzzammil (Dept. of Electronic Engineering, Inha University) ;
-
Yun, Byung-Choon
(Dept. of Electronic Engineering, Inha University) ;
-
Kim, Deok-Hwan
(Dept. of Electronic Engineering, Inha University)
- Published : 2010.11.12
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%.
Keywords
- unsupervised image segmentation;
- multispectral image;
- remote sensing;
- combined neighborhood differences