Browse > Article
http://dx.doi.org/10.7848/ksgpc.2011.29.1.71

Unsupervised Change Detection of Hyperspectral images Using Range Average and Maximum Distance Methods  

Kim, Dae-Sung (건국대학교 신기술융합학과)
Kim, Yong-Il (서울대학교 건설환경공학부)
Pyeon, Mu-Wook (건국대학교 신기술융합학과)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.29, no.1, 2011 , pp. 71-80 More about this Journal
Abstract
Thresholding is important step for detecting binary change/non-change information in the unsupervised change detection. This study proposes new unsupervised change detection method using Hyperion hyperspectral images, which are expected with data increased demand. A graph is drawn with applying the range average method for the result value through pixel-based similarity measurement, and thresholding value is decided at the maximum distance point from a straight line. The proposed method is assessed in comparison with expectation-maximization algorithm, coner method, Otsu's method using synthetic images and Hyperion hyperspectral images. Throughout the results, we validated that the proposed method can be applied simply and had similar or better performance than the other methods.
Keywords
Automatic Thresholding; Range Average; Unsupervised Change Detection; Hyperspectral Images; Maximum Distance;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Singh, A. (1989), Digital Change Detection Techniques Using Remotely Sensed Data, International Journal of Remote Sensing, IJRS, Vol. 10, No. 6, pp. 989-1003.   DOI   ScienceOn
2 Wu, Q. Z., Chen, H. Y., and Jeng, B. S. (2005), Motion Detection via Change-point Detection for Cumulative histograms of ratio images, Pattern Recognition Letters, Vol. 26, pp. 555-563.   DOI   ScienceOn
3 Bazi, Y., Bruzzone, L., and Melgani, F. (2007), Image thresholding based on the EM algorithm and the generalized Gaussian distribution, Pattern Recognition archive, Vol. 40, No. 2, pp. 619-634.   DOI   ScienceOn
4 Bruzzone, L., and Prieto, D. F. (2000), Automatic Analysis of the Difference Image for Unsupervised Change Detection, IEEE Transactions on Geoscience and Remote Sensing, IEEE, Vol. 38, No. 3, pp. 1171-1182.   DOI   ScienceOn
5 Castellana, L., D'Addabbo, A., and Pasquariello, G. (2007), A Composed Supervised/unsupervised Approach to Improve Change Detection from Remote Sensing, Pattern Recognition Letters, IEEE, Vol. 28, No. 4, pp. 405-413.   DOI   ScienceOn
6 Frank, M., and Canty, M. (2003), Unsupervised Change Detection for Hyperspectral Images, JPL Publication, JPL, 8th publication.
7 Lowe, D. G. (2004), Distinctive Image Features from Scaleinvariant Keypoints, International Journal on Computer Vision, IJCV, Vol. 60, No. 2, pp. 91-110.   DOI
8 Lu, D., Mausel, P., brondizio, E., and Moran, E. (2004), Change Detection Techniques, International Journal of Remote Sensing, IJRS, Vol. 25, No. 12, pp. 2365-2407.   DOI   ScienceOn
9 Luthon, F., Lievin, M., and Faux, F. (2004), On the Use of Entropy Power for Threshold Selection, Signal Processing, Vol. 84, No. 10, pp. 1789-1804.   DOI   ScienceOn
10 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, Vol. 8, No. 1, pp. 3-17.   DOI   ScienceOn
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, ISPRS, Vol. 54, No. 4, pp. 221-233.   DOI   ScienceOn
12 Otsu, N. (1979), A Threshold Selection Method from Graylevel Histograms, IEEE Transactions on Systems, Man, and Cybernetics, IEEE, Vol. 9, pp. 62?66.   DOI   ScienceOn
13 박노욱, 지광훈, 이광재, 권병두 (2003), 다중시기 원격탐사 화상의 변화탐지를 위한 임계치 자동 추정, 대한원격탐사학회지, 대한원격탐사학회, 제 19권, 제 6호, pp. 465-478.   DOI
14 Rechard, J. R., Srinvias, A., Omar, A., and Radrinath, R. (2005), Image Change Detection Algorithms: A Systematic Survey, IEEE Transactions on Image Processing, IEEE, Vol. 14, No. 3, pp. 294-307.   DOI   ScienceOn
15 Rosin, P. L. (2001), Unimodal Thresholding, Pattern Recognition, Vol. 34, pp. 2083-2096.   DOI   ScienceOn
16 김대성, 김형태 (2008), 누적 유사도 측정을 이용한 자동 임계값 결정 기법 - 다중분광 및 초분광영상의 무감독 변화 탐지를 목적으로, 대학원격탐사학회지, 대한원격탐사학회, 제 24권, 제 4호, pp. 341-349.