An automatic unsupervised method based on context-sensitive spectral angle mapper for change detection of remote sensing images

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8933, Issue , 2014, Pages 151-162

  Moughal, Tauqir Ahmed ^a,b   Yu, Fusheng ^a  

^a BEIJING NORMAL UNIVERSITY   (China)

^b ALLAMA IQBAL OPEN UNIVERSITY   (Pakistan)

Author keywords

Change detection; Remote sensing; Spatio contextual information; Spectral angle mapper

Indexed keywords

K-MEANS CLUSTERING; PIXELS;

CHANGE DETECTION; CONTEXT-SENSITIVE; CONTEXTUAL INFORMATION; DECISION LEVELS; LANDSAT-5 TM IMAGES; REMOTE SENSING IMAGES; SPECTRAL ANGLE MAPPERS; UNSUPERVISED METHOD;

REMOTE SENSING;

EID: 84918838814     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-14717-8_12     Document Type: Article

References (27)

1. Singh, A.: Digital Change Detection Techniques using Remotely Sensed Data. Int. J. Remote Sens. 10, 989–1003 (1989)
2. Im, J., Jensen, J.R.: A Change Detection Model Based on Neighborhood Correlation Image Analysis and Decision Tree Classification. Remote Sens. Environ. 99, 326–340 (2005)
3. Chan, J.C.W., Chan, K.P., Yeh, A.G.O.: Detecting the Nature of Change in an Urban Environment: A Comparison of Machine Learning Algorithms. Photogramm. Eng. Rem. S. 67, 213–225 (2001)
4. Sezgin, M., Sankur, B.: A Survey over Image Thresholding Techniques and Quantitative Performance Evaluation. J. Electron. Image. 13, 146–165 (2004)
5. Xue, J.H., Zhang, Y.J.: Ridler and Calvard’s, Kittler and Illingworth’s and Otsu’s Methods for image Thresholding. Pattern Recognition Letters 33, 793–797 (2012)
6. Rosin, P.L., Ioannidis, E.: Evaluation of Global Image Thresholding for Change Detection. Pattern Recognition Letters 24, 2345–2356 (2003)
7. Orlando, J.T., Rui, S.: Image Segmentation by Histogram Thresholding Using Fuzzy Sets. IEEE Trans. Image Processing. 11, 1457–1465 (2002)
8. Du, P., Liu, S., Gamba, P., Tan, K., Xia, J.: Fusion of Difference Images for Change Detection over Urban Areas. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5, 1076–1086 (2012)
9. Du, P., Liu, S., Xie, J., Zhao, Y.: Information Fusion Techniques for Change Detection from Multi-temporal Remote Sensing Images. Information Fusion 14, 19–27 (2013)
10. Celik, T.: Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k-Means Clustering. IEEE Geoscience and Remote Sensing Letters 6, 772–776 (2009)
11. Deng, J.S., Wang, K., Deng, Y.H., Qi, G.J.: PCA based Land use Change Detection and Analysis using Multitemporal and Multisensor Satellite Data. Int. J. Remote Sens. 29, 4823–4838 (2008)
12. Im, J.: Neighborhood Correlation Image Analysis for Change Detection Using Different Spatial Resolution Imagery. Korean Journal of Remote Sensing 22, 337–350 (2006)
13. Teng, S.P., Chen, Y.K., Cheng, K.S., Lo, H.C.: Hypothesis-test-based Landcover Change Detection using Multi-temporal Satellite Images – A Comparative Study. Adv. Space Res. 41, 1744–1754 (2008)
14. Meola, J., Moses, R.L.: Detecting Changes in Hyperspectral Imagery Using a Model- Based Approach. IEEE Trans. Geosci. Remote Sens. 49, 2647–2661 (2011)
15. Krylov, V.A., Moser, G., Voisin, A., Serpico, S.B., Zerubia, J.: Change Detection with Synthetic Aperture Radar Images by Wilcoxon Statistic Likelihood Ratio Test. In: 19th IEEE International Conference on Image Processing, pp. 2093–2096 (2012)
16. Lu, D., Mausel, P., Brondízio, E., Moran, E.: Change Detection Techniques. Int. J. Remote Sens. 25, 2365–2407 (2004)
17. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B.: Digital Change Detection Methods in Ecosystem Monitoring: A Review. Int. J. Remote Sens. 25, 1565–1596 (2004)
18. Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image Change Detection Algorithms: A Systematic Survey. IEEE Trans. Image Process 14, 294–307 (2005)
19. Bhagat, V.S.: Use of Remote Sensing Techniques for Robust Digital Change Detection of Land: A Review. Recent Patents on Space Technology 2, 123–144 (2012)
20. Hussain, M., Chen, D., Cheng, A., Wei, H., Stanley, D.: Change Detection from Remotely Sensed Images: From Pixel-based to Object-based Approaches. ISPRS J. Photogramm. 80, 91–106 (2013)
21. Kasetkasem, T., Varshney, P.K.: An Image Change Detection Algorithm based on Markov Random Field Models. IEEE Trans. Geosci. Remote Sens. 40, 181–1823 (2002)
22. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. J. Roy. Stat. Soc. 39, 1–38 (1977)
23. Meer, F.V.D.: The Effectiveness of Spectral Similarity Measures for the Analysis of Hyperspectral Imagery. Int. J. Appl. Earth Obs. 8, 3–17 (2006)
24. Schiefer, S., Hostert, P., Damm, A.: Correcting Brightness Gradients in Hyperspectral Data from Urban Areas. Remote Sens. Environ. 101, 25–37 (2006)
25. Leeuw, H.D., Jia, H., Yang, L., Liu, X., Schmidt, K., Skidmore, A.K.: Comparing Accuracy Assessments to Infer Superiority of Image Classification Methods. Int. J. Remote Sens. 27, 223–232 (2006)
26. Schmidt, F., Doute, S., Schmitt, B.: WAVANGLET: An Efficient Supervised Classifier for Hyperspectral Images. IEEE Trans. Geosci. Remote Sens. 45, 1374–1385 (2007)
27. Healey, S.P., Cohen, W.B., Zhiqiang, Y., Krankina, O.N.: Comparison of Tasseled Capbased Landsat Data Structures for use in Forest Disturbance Detection. Remote Sens. Environ. 97, 301–310 (2005)