A new approach to change vector analysis using distance and similarity measures

Remote Sensing

Volumn 3, Issue 11, 2011, Pages 2473-2493

  Carvalho Júnior, Osmar A ^a   Guimarães, Renato F ^a   Gillespie, Alan R ^b   Silva, Nilton C ^c   Gomes, Roberto A T ^a  

^a UNIVERSITY OF BRASILIA   (Brazil)

^b UNIVERSITY OF WASHINGTON   (United States)

^c Universidade Evangélica de Goiás   (Brazil)

Author keywords

Bi temporal; Change detection; Euclidean distance; Mahalanobis distance; Spectral angle mapper; Spectral correlation mapper

Indexed keywords

BI-TEMPORAL; CHANGE DETECTION; EUCLIDEAN DISTANCE; MAHALANOBIS DISTANCES; SPECTRAL ANGLE MAPPERS; SPECTRAL CORRELATION;

FACE RECOGNITION;

EID: 84856764377     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs3112473     Document Type: Article

References (37)

1. Malila, W.A. Change Vector Analysis: An Approach for Detecting Forest Changes with Landsat. In Proceedings of 6th Annual Symposium on Machine Processing of Remotely Sensed Data Soil Information Systems and Remote Sensing and Soil Survey, West Lafayette, IN, USA, 3-6 June 1980; pp. 326-335.
2. Crist, E.P.; Cicone, R.C. Application of the Tasseled Cap concept to simulated thematic mapper data. Photogramm. Eng. Remote Sensing 1984, 50, 343-352.
3. Xian, G.; Homer, C.; Fry, J. Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods. Remote Sens. Environ. 2009, 113, 1133-1147.
4. Virag, L.A.; Colwell, J.E. An Improved Procedure for Analysis of Change in Thematic Mapper Image-Pairs. In Proceedings of 21st International Symposium on Remote Sensing of Environment, Ann Arbor, MI, USA, 26-30 October 1987; pp. 1101-1110.
5. Allen, T.R.; Kupfer, J.A. Application of spherical statistics to change vector analysis of Landsat data: Southern Appalachian spruce-fir forests. Remote Sens. Environ. 2000, 74, 482-493.
6. Chen, J.; Gong, P.; He, C.; Pu, R.; Shi, P. Land-use/land-cover change detection using improved change-vector analysis. Photogramm. Eng. Remote Sensing 2003, 69, 369-379.
7. Warner, T. Hyperspherical direction cosine change vector analysis. Int. J. Remote Sens. 2005, 26, 1201-1215.
8. Lambin, E.F.; Strahler, A.H. Change-Vector analysis in multitemporal space: A tool to detect and categorize land-cover change processes using high temporal-resolution satellite data. Remote Sens. Environ. 1994, 48, 231-244.
9. Ridd, M.K.; Liu, J. A comparison of four algorithms for change detection in an urban environment. Remote Sens. Environ. 1998, 63, 95-100.
10. Kaimowitz, D.; Smith, J. Soybean technology and the loss of natural vegetation in Brazil and Bolivia. In Agricultural Technologies and Tropical Deforestation; Angelsen, A., Kaimowitz, D., Eds.; CABI Publishing: New York, NY, USA, 2001; pp. 195-211.
11. Smith, J.; Winograd, M.; Gallopin, G.; Pachico, D. Dynamics of the agricultural frontier in the Amazon and savannas of Brazil: analyzing the impact of policy and technology. Environ. Model. Assess. 1998, 3, 31-46.
12. Vassilakis, E. Remote sensing of environmental change in the Antirio deltaic fan region, Western Greece. Remote Sens. 2010, 2, 2547-2560.
13. Almutairi, A.; Warner, T.A. Change detection accuracy and image properties: A study using simulated data. Remote Sens. 2010, 2, 1508-1529.
14. Townshend, J.R.G.; Justice, C.O.; Gurney, C.; McManus, J. The impact of misregistration on change detection. IEEE Trans. Geosci. Remote. Sens. 1992, 30, 1054-1060.
15. Dai, X.; Khorram, S. The effects of image misregistration on the accuracy of remotely sensed change detection. IEEE Trans. Geosci. Remote. Sens. 1998, 36, 1566-1577.
16. Du, Y.; Teillet, P.M.; Cihlar, J. Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection. Remote Sens. Environ. 2002, 82, 123-134.
17. Casselles, V.; Lopez Garcia, M.J An alternative simple approach to estimate atmospheric correction in multitemporal studies. IEEE Trans. Geosci. Remote Sens. 1989, 10, 1127-1134.
18. Hall, F.G.; Strebel, D.E.; Nickeson, J.E.; Goetz, S.J. Radiometric rectification: Toward a common radiometric response among multidate, multisensor images. Remote Sens. Environ. 1991, 35, 11-27.
19. Hill, J.; Sturm, B. Radiometric correction of multitemporal thematic mapper data for use in agricultural landcover classification and vegetation monitoring. Int. J. Remote Sens. 1991, 12, 1471-1491.
20. Adams, J.B.; Gillespie, A.R. Remote Sensing of Landscapes with Spectral Images: A Physical Modeling Approach; Cambridge University Press: New York, NY, USA, 2006; p. 362.
21. Kruse, F.A.; Lefkoff, A.B.; Boardman, J.W.; Heiedbrecht, K.B.; Shapiro, A.T.; Barloon, P.J.; Goetz, A.F.H. The Spectral Image Processing System (SIPS)-Interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 1993, 44, 145-163.
22. Baugh, W.M.; Kruse, F.A.; Atkinson, W.W., Jr. Quantitative geochemical mapping of ammonium minerals in the southern cedar mountains, Nevada, using the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sens. Environ. 1998, 65, 292-308.
23. Rowan, L.C.; Mars, J.C. Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Remote Sens. Environ. 2003, 84, 350-366.
24. Pinori, S.; Bellucci, G. Imaging spectroscopy of selected regional dark mantle deposits of the Moon. Planet Space Sci. 2001, 49, 487-500.
25. Carvalho Júnior, O.A.; Meneses, P.R. Spectral Correlation Mapper (SCM): An Improving Spectral Angle Mapper. In Proceedings of Annual JPL Airborne Earth Science Workshop, Pasadena, CA, USA, 23-25 February 2000; JPL-Publication 00-18; pp. 65-74.
26. Fung, T.; Le Drew, E. Application of principal components analysis to change detection. Photogramm. Eng. Remote Sensing 1987, 53, 1649-1658.
27. Jensen, J.R. Introductory Digital Image Processing; Prentice-Hall: Englewood Cliffs, NJ, USA, 1986.
28. Congalton, R.; Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices; CRC/Lewis Press: Boca Raton, FL, USA, 1999.
29. Biging, G.S.; Colby, D.R.; Congalton, R.G. Sampling systems for change detection accuracy assessment. In Remote Sensing Change Detection: Environmental Monitoring Methods and Applications; Lunetta, R.S., Elvidge, C.D., Eds.; Taylor and Francis: London, UK, 1999; pp. 281-308.
30. Gates, D.M.; Keegan, H.J.; Schleter, J.C.; Weidner, V.R. Spectral properties of plants. Appl. Optics 1965, 4, 11-20.
31. Gausman, H.W. Leaf reflectance of near infrared. Photogramm. Eng. Remote Sensing 1974, 40, 183-191.
32. Woolley, J.T. Reflectance and transmittance of light by leaves. Plant Physiol. 1971, 47, 656-662.
33. Wilson, E.H.; Sader, S.A. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sens. Environ. 2002, 80, 385-396.
34. Cohen, W.B.; Fiorella, M.; Gray, J.; Helmer, E.; Anderson, K. An efficient and accurate method for mapping forest clearcuts in the Pacific Northwest using Landsat imagery. Photogramm. Eng. Remote Sensing 1998, 64, 293-300.
35. Alberga, V. Similarity measures of remotely sensed multi-sensor images for change detection applications. Remote Sens. 2009, 1, 122-143.
36. Im, J.; Jensen, J.R. A change detection model based on neighborhood correlation image analysis and decision tree classification. Remote Sens. Environ. 2005, 99, 326-340.
37. Çelik, T.; Yetgin, Y. Change detection without difference image computation based on multiobjective cost function optimization. Turk. J. Elec. Eng. Comp. Sci. 2011, 19, 941-956.