LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Shi, Xiaodan; Ma, Guorui; Chen, Fenge; Ma, Yanli (2016)
Publisher: Copernicus Publications
Languages: English
Types: Article
Subjects: TA1-2040, T, TA1501-1820, Applied optics. Photonics, Engineering (General). Civil engineering (General), Technology
This paper presents a kernel-based approach for the change detection of remote sensing images. It detects change by comparing the probability density (PD), expressed as kernel functions, of the feature vector extracted from bi- temporal images. PD is compared by defined kernel functions without immediate PD estimation. This algorithm is model-free and it can process multidimensional data, and is fit for the images with rich texture in particular. Experimental results show that overall accuracy of the algorithm is 98.9 %, a little bit better than that of the change vector analysis and classification comparison method, which is 96.7 % and 95.9 % respectively.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • WOODCOCK, C.E., MACOMBER, S.A., PAX-LENNEY, M. and COHEN, W.B., 2001, Monitoring large areas for forest change using Landsat: generalization across space-time and Landsat sensors, Remote sensing of Environment, 78: 194-203.
    • COPPIN, P. and BAUER, M., 1994, Processing of multitemporal Landsat TM imagery to optimize extraction of forest cover change features. IEEE Transactions on Geoscience and Remote Sensing, 32: 918-927.
    • MUCHONEY, D.M. and HAACK, B.N., 1994, Change detection for monitoring forest defoliation, Photogrammetric Engineering and Remote Sensing, 60(10): 1243- 1251.
    • PILON, P.G., HOWARTH, P.J. and BULLOCK, R.A., 1988, An enhanced classification approach to change detection in semi-arid environments. Photogrammetric Engineering and Remote Sensing, 54(12): 1709-1716.
    • RIDD, M.K. and LIU J.J, 1998, A comparison of four algorithms for change detection in an urban environment, Remote Sensing of Environment, 65(2): 95-100.
    • ROY, P.S., RANGANATH, B.K., DIWAKAR, P.G., VOHRA, T.P.S., BHAN, S.K., SINGH, I.J. and PANDIAN, V.C., 1991, Tropical forest mapping and monitoring using remote sensing, International Journal of Remote Sensing, 12(11): 2205- 2225.
    • SADER, S.A., POWELL, G.V.N., and RAPPOLE, J.H., 1991, Migratory bird habitat monitoring through remote sensing, International Journal of Remote Sensing, 12(3): 363-372.
    • VOGELMANN, J.E., 1988, Detection of forest change in the green mountains of Vermont using multispectral scanner data, International Journal of Remote Sensing, 9(7): 1187- 1200.
    • DU P.J., LIU S., LIU P., TAN K. & CHENG L., 2014, Subpixel change detection for urban land-cover analysis via multi-temporal remote sensing images, Geo-spatial Information Science, 17(1):26-38.
    • BOVLO, F. and BRUZZONE, L., 2007, A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain, IEEE Transactions on Geoscience and Remote Sensing, 45(1): 218-235.
    • NEMMOUR, H., CHIBANI Y., 2006, Multiple support vector machines for land cover change detection: An application for mapping urban extensions. International Society for Photogrammetry and Remote Sensing , 61(2): 125-133.
    • LU, D., MAUSEL, P., BRONDIZIO, E., MORAN, E., 2004, Change detection techniques. International Journal of Remote Sensing, 25(12): 2365-2407.
    • RICHARD, J.R., ANDRA, S., Al-KOFAHI, O., ROYSAM, B., 2005, Image Change Detection Algorithms:A systematic Survey, IEEE TRANSACTIONS ON IMAGE PROCESSING, 14(3): 294-307.
    • SCHOLKOPF, B., PLATT J.C., SHAWE-TAYLOR, J., SMOLA, A.J., 2001, Estimating the Support of a Highdimensional Distribution. Neural Computation, 13(7): 1443-1471.
    • SUI, H.G., ZHOU, Q.M., GONG, J.Y, MA, G.R., 2008, Processing of multi-temporal data and change detection, Advances in Photogrammetry, Remote Sensing and Spatial Information Sciences: 2008 International Society for Photogrammetry and Remote Sensing Congress Book, Edited by Zhilin Li, Jun Chen and Emmanuel Baltsavias, 227-247.
    • SCHOLKOPF, B., MIKA, S., BURGES, C.J.C., KNIRSCH, P., MULLER, K.R., RATSCH, G., SMOLA, A.J., 1999, Input Space versus Feature Space in KernelBased Methods. IEEE TRANSACTIONS ON NEURAL NETWORKS, 10(5): 1000-1017.
  • No related research data.
  • No similar publications.