Browse > Article
http://dx.doi.org/10.7780/kjrs.2008.24.4.341

Automatic Thresholding Method using Cumulative Similarity Measurement for Unsupervised Change Detection of Multispectral and Hyperspectral Images  

Kim, Dae-Sung (Department of Civil & Environmental Engineering, Seoul National University)
Kim, Hyung-Tae (Korea Land Corporation, Land urban Research Institute)
Publication Information
Korean Journal of Remote Sensing / v.24, no.4, 2008 , pp. 341-349 More about this Journal
Abstract
This study proposes new automatic thresholding method, which is important step for detecting binary change/non-change information using satellite images. Result value through pixel-based similarity measurement is calculated cumulatively with regular interval, and thresholding is pointed at the steep slope position. The proposed method is assessed in comparison with expectation-maximization algorithm and coner method using synthetic images, ALI images, and Hyperion images. Throughout the results, we validated that our method can guarantee the similar accuracy with previous algorithms. It is simpler than EM algorithm, and can be applied to the binormal histogram unlike the coner method.
Keywords
Automatic Thresholding; Cumulative Similarity Measurement; Unsupervised Change Detection; Hyperspectral Images; EM Algorithm; Comer Method;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 박노욱, 지광훈, 이광재, 권병두, 2003. 다중시기 원격탐사 화상의 변화탐지를 위한 임계치 자동 추정, 대한원격탐사학회지, 19(6): 465-478   DOI
2 Castellana, L., A. D'Addabbo, and G. Pasquariello, 2007. A Composed Supervised/unsupervised Approach to Improve Change Detection from Remote Sensing, Pattern Recognition Letters, 28(4): 405-413   DOI   ScienceOn
3 Rechard, J. R., A. Srinvias, A. Omar, and R. Radrinath, 2005. Image Change Detection Algorithms: A Systematic Survey, IEEE Transactions on Image Processing, 14(3): 294-307   DOI   ScienceOn
4 Rosin, P. L. and J. Hervas, 2005. Remote Sensing Image Thresholding Methods for Detemining Landslide Activity, International Journal of Remote Sensing, 26(6): 1075-1092   DOI   ScienceOn
5 Bruzzone, L. and D. F. Prieto, 2000b. A Minimum-cost Thresholding Technique for Unsupervised Change Detection, International Journal of Remote Sensing, 21(18): 3539-3544   DOI
6 김대성, 김용일, 2006. 화소간 유사도 측정 기법을 이용한 하이퍼스펙트럴 데이터의 무감독 변화탐지에 관한 연구, 춘계학술대회 발표회 논문집, 한국측량학회, 243-248
7 Singh, A., 1989. Digital Change Detection Techniques Using Remotely Sensed Data, International Journal of Remote Sensing, 10(6): 989-1003   DOI   ScienceOn
8 Lu, D., P. Mausel, E. brondizio, and E. Moran, 2004. Change Detection Techniques, International Journal of Remote Sensing, 25(12): 2365-2407   DOI   ScienceOn
9 김선화, 이규성, 마정림, 국민정, 2005. 초분광 원격탐사의 특성, 처리기법 및 활용 현황, 대한원격탐사학회지, 21(4): 341-369   과학기술학회마을   DOI
10 Frank, M. and M. Canty, 2003. Unsupervised Change Detection for Hyperspectral Images, JPL Publication, 8th publication
11 Metternicht, G., 1999. Change Detection Assessment using Fuzzy Set and Remotely Sensed Data: an Application of Topographic Map Revision, ISPRS Journal of Photogrammetry and Remote Sensing, 54(4): 221-233   DOI   ScienceOn
12 Barry, P., 2001. EO-1/Hyperion Science Data User's Guide. TRW Space, Defense and Information Systems
13 김대성, 김용일, 어양담, 2007. 변화탐지를 위한 Hyperion 초분광 영상의 자동 기하보정과 밴드선택에 관한 연구, 한국측량학회지, 25(5): 383-392   과학기술학회마을
14 Luthon, F., M. Lievin, and F. Faux, 2004. On the Use of Entropy Power for Threshold Selection, Signal Processing, 84(10): 1789-1804   DOI   ScienceOn
15 Bazi, Y., L. Bruzzone, and F. Melgani, 2007. Image thresholding based on the EM algorithm and the generalized Gaussian distribution, Pattern Recognition archive, 40(2): 619-634   DOI   ScienceOn
16 Carvalho, O. A. D. and P. R. Meneses, 2000. Spectral Correlation Mapper (SCM): An Improving Spectral Angle Mapper, Summaries of the Ninth JPL Airborne Earth Science Workshop, Pasadena, CA, 2000, vol. 1, pp. 65-74
17 Bruzzone, L. and D. F. Prieto, 2000a. Automatic Analysis of the Difference Image for Unsupervised Change Detection, IEEE Transactions on Geoscience and Remote Sensing, 38(3): 1171-1182   DOI   ScienceOn
18 Meer, F. V. D., 2006. The Effectiveness of Spectral Similarity Measures for the Analysis of Hyperspectral Imagery, International Journal of Applied Earth Observation and Geoinforrmation, 8(1): 3-17   DOI   ScienceOn