Open Access Open Access  Restricted Access Subscription or Fee Access

Earthquake Damage Assessment using Multi Temporal Satellite Images

Dr. Sanjay K. Jain

Abstract


The work presented here is concerned with the problem of Earthquake damage assessment using multi temporal satellite images. Earthquake is one of the unavoidable natural hazards that cause lots of damages and problems to the economy, environment and the whole life of the people. After earthquakes, there is a need for rapid, accurate and reliable damage information in the critical post event hours to guide response activities. Disaster damage assessment using remotely sensed data can be carried out using the multi temporal approach, which requires two images pre-damage and post-damage of the affected area that are compared to identify changes. In the present work, we have performed the image fusion and image change detection for precise Earthquake damage assessment. We have proposed an IHS and Wavelet transform based integrated image fusion technique for fusion of pre and post panchromatic and multispectral satellite images. The resultant pre and post fused images of earthquake disaster are the input to the image change detection method. We have also proposed a novel Minimum Description Length (MDL) based method for change detection in pre and post images of the earthquake. The results generated by the image fusion and change detection methods are quite helpful for earthquake damage assessment.

Keywords


Image Fusion, Change Detection, Remote Sensing, Damage Assessment.

Full Text:

PDF

References


Blum R.S. and Liu Z., ―Multi-Sensor Image Fusion and Its Applications,‖ special series on Signal Processing and Communications, CRC Press: Boca Raton, FL, USA, 2005.

Bruzzone L. and Prieto D. F., ―An adaptive semi-parametric and context- based approach to unsupervised change detection in multi temporal remote-sensing images,‖ IEEE Transaction Image Processing, vol. 11, no. 4, 2002, pp. 452–466.

Candes E.J. and Donoho D.L., ―Curvelets-A Surprisingly Effective Non-adaptive Representation for Objects with Edges,‖ Curves and Surfaces, Vanderbilt University Press: Nashville, TN, USA, 2000, pp. 105–120.

Colwell J. E. and Weber F. P., ―Forest change detection,‖ in Proc. 15th International Symposium Remote Sensing of Environment, 1981, pp. 839–852.

Dasarathy B.V., ―A special issue on image fusion: advances in the state of the art,‖ International Journal of Information Fusion, vol 8, no. 2, april, 2007, pp. 113.

Distefano L., Mattoccia S., and Mola M., ―A change detection algorithm based on structure and color,‖ in Proc. IEEE Conf. Advanced Video and Signal Based Surveillance, 2003, pp. 252–259.

Dong J., Yang X., Clinton N. and Wang N., ―An artificial neural network model for estimating crop yields using remotely sensed information,‖ International Journal of Remote Sensing, vol. 25, 2004, pp. 1723–1732.

Ganzalo P. and Jesus M.A., ―Wavelet-based image fusion tutorial,‖ Pattern Recognition.vol. 37, 2004, pp. 1855–1872.

Hazel G. G., ―Object-level change detection in spectral imagery‖, IEEE Trans. Geo Science and Remote Sensing, vol. 39, no. 3, 2004, pp. 553-561.

Kasetkasem T. and Varshney P. K., ―Image change detection algorithm based on Markov random field models,‖ IEEE Trans. Geo Science and Remote Sensing, vol. 40, no. 8, 2002, pp. 1815-1823.

Krista A., Yun Z. and Peter D., ―Wavelet based image fusion techniques–An introduction, review and comparison,‖ International Journal of Photogrammetric Engineering & Remote sensing vol. 62, 2007, pp. 249–263.

Louis E.K. and Yan X.H., ―A neural network model for estimating sea surface chlorophyll and sediments from thematic mapper imagery,‖ International Journal of Remote Sensing Environment. Vol. 66, 1998, pp. 153–165.

Ma H., Jia C.Y. and Liu S., ―Multisource image fusion based on wavelet transforms fusion,‖ International Journal of Information Technology, vol. 11, 2005, pp. 81–91.

Malila W. A., ―Change vector analysis: An approach for detecting forest changes with Landsat,‖ in Proc. 6th Annual Symp. Machine Processing of Remotely Sensed Data, 1980, pp. 326–335.

Mallat S.G. (1989), ―Theory for multi resolution signal decomposition: the wavelet representation,‖ IEEE Trans. Pattern Analysis Machine Intelligence, pp. 674–693.

Ozisik D., ―Post earth quake damage assessment using satellite and serial video imagery,‖ M.S. Thesis , 2004, pp. 1-145.

Pohl, C. and Van Genderen, ―Multi sensor image fusion in remote sensing: concepts, methods and applications,‖ International Journal of Remote Sensing Vol. 19, 1998, pp. 823–854.

Pouran, B., ―Comparison between four methods for data fusion of ETM+ multispectral and pan images,‖ International Journal of Geo-spatial Information Science. Vol. 8, 2005, pp. 112–122.

Rissanen J., ―Minimum-description-length principle,‖ in Encyclopedia of Statistical Sciences, 5th ed. John Wiley, 1987, pp. 523–527.

Simone G., Farina A., Morabito F.C., Serpico S.B. and Bruzzone L., ―Image fusion techniques for remote sensing applications,’ International Journal of Image Fusion vol. 3,2002, pp. 3–15.

Singh A., ―Digital change detection techniques using remotely-sensed data,‖ International Journal of Remote Sensing., vol. 10, no. 6, 1989, pp. 989–1003.

Vijayaraj V., Younan N. and Hara C., ―Concepts of image fusion in remote sensing applications,‖ In Proceedings of IEEE International Conference on Geo science and Remote Sensing Symposium, Denver, CO, USA, 2006, pp. 3798–3801.

Yun Z., ―Understanding image fusion,‖ International Journal of Photogrammetric Engineering and Remote Sensing, vol. 6, 2004, pp. 657–661.

Zhang Y. and Hong G., ―An HIS and wavelet integrated approach to improve pan-sharpening visual quality of natural color IKONOS and QuickBird image fusion,‖ International Journal of Information Fusion, vol. 6, 2005, pp. 225–234.

http://www.digitalglobe.com/index.php/27/Sample+Imagery

http://en.wikipedia.org/wiki/Natural _Disaster

http://en.wikipedia.org/wiki/Earthquake


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.