A NEW CHANGE DETECTION APPROACH BASED ON WAVELET TRANSFORMATION AND GAUSSIAN MIXTURE MODELS FOR OPTICAL IMAGERY IN DISASTER MANAGEMENT

Year 2018, , 171 - 179, 31.01.2018

Ümit Haluk Atasever

https://doi.org/10.28948/ngumuh.386438

Abstract

   Disasters such as forest fires and floods are among most important
problems of both our country and the world. In order to be able to perform
rapid rehabilitation processes after disaster, damaged areas should be
determined with high accuracy quickly. In this study, a new approach, designed
for optical images, based on wavelet transform and Gaussian mixture models is
proposed for detection of damaged areas after disasters such as fire and flood.
In the first step of the presented approach, standard and logarithmic
difference images from images belonging before and after disaster are
calculated. Second, median filter to standard difference image and wiener
filter to logarithmic difference image are applied, respectively.  After that, these images are fused with
wavelet transformation. Lastly, fused image is clustered with Gaussian mixture
models and thus the areas damaged by the disasters are identified.  The effectiveness of the approach was
explored using Sardinia and Mexico data sets resulting from real disasters. The
performance of the proposed approach has been investigated and its success has
been shown with the mean squared error, peak signal to noise ratio, structural
similarity index and universal quality index metrics, in addition to the total
error and total error rate criteria.

Keywords

Disaster, Change Detection, Wavelet Transformation, Gaussian Mixture Models

AFET YÖNETİMİNDE OPTİK GÖRÜNTÜLER İÇİN DALGACIK DÖNÜŞÜMÜ VE GAUSSIAN KARIŞIM MODELLERİ TABANLI YENİ BİR DEĞİŞİM SAPTAMA YAKLAŞIMI

Abstract

   Orman yangını, taşkın gibi felaketler hem ülkemizin hem
de dünyanın en önemli problemleri arasındadır Afet sonrası alansal
rehabilitasyon işlemlerinin hızla yapılabilmesi için zarar gören alanların
hızlı ve yüksek doğrulukla belirlenebilmesi gerekmektedir.  Bu çalışmada, optik görüntüler için
tasarlanmış, yangın ve sel gibi afetler sonrasında hasar gören alanların tespit
edilmesine yönelik dalgacık dönüşümü ve Gaussian karışım modelleri temelli yeni
bir yaklaşım önerilmiştir. Sunulan yaklaşımın ilk aşamasında, afet öncesi ve
sonrasına ait görüntülerin standart ve logaritmik fark görüntüleri hesaplanır.
İkinci aşamada standart fark görüntüsüne medyan filtre, logaritmik fark
görüntüsüne wiener filtre uygulanır. Ardından bu görüntüler dalgacık dönüşümü
ile birleştirilir. Son aşamada ise birleştirilmiş görüntü Gaussian karışım
modelleri ile kümelenir ve böylelikle afet nedeniyle zarar gören alanlar tespit
edilmiş olur. Yaklaşımın etkinliği gerçek afetler neticesinde ortaya çıkan
Sardinia ve Mexico veri setleri kullanılarak irdelenmiştir. Önerilen yaklaşımın
performansı toplam hata ve toplam hata oranı kriterlerine ek olarak ortalama karesel hata tepe gürültü sinyal oranı, yapısal benzerlik indeksi ve evrensel kalite indeksi metrikleri ile incelenmiş ve
başarısı ortaya koyulmuştur.

Keywords

Afet, Değişim Saptama, Dalgacık Dönüşümü, Gaussian Karışım Modelleri

References

• [1] VAN WESTEN, C.J., 3.10 Remote Sensing and GIS for Natural Hazards Assessment and Disaster Risk Management A2 - Shroder, John F, in Treatise on Geomorphology, ed San Diego, pp. 259-298 Academic Press, 2013.
• [2] HUYCK, C., VERRUCCI, E., BEVINGTON, J., Chapter 1 - Remote Sensing for Disaster Response: A Rapid, Image-Based Perspective A2 - Shroder, John F, in Earthquake Hazard, Risk and Disasters, Wyss, M., Ed., ed Boston, pp. 1-24 Academic Press, 2014.
• [3] ATASEVER, U.H., CIVICIOGLU, P., BESDOK, E., OZKAN, C., "A New Unsupervised Change Detection Approach Based on DWT Image Fusion And Backtracking Search Optimization Algorithm for Optical Remote Sensing Data", Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7: 15-18, 2014.
• [4] ATASEVER, U.H., KESIKOGLU, M.H., OZKAN, C., "A New Artificial Intelligence Optimization Method for Pca Based Unsupervised Change Detection of Remote Sensing Image Data", Neural Network World, 26(2): 141-154, 2016.
• [5] ZHENG, Y., ZHANG, X., HOU, B., LIU, G., "Using Combined Difference Image and K-Means Clustering for SAR Image Change Detection", IEEE Geoscience and Remote Sensing Letters, 11(3): 691-695, 2014.
• [6] SUBUDHI, B.N., BOVOLO, F., GHOSH, A., BRUZZONE, L., "Spatio-Contextual Fuzzy Clustering with Markov Random Field Model for Change Detection in Remotely Sensed Images", Optics & Laser Technology, 57: 284-292, 2014.
• [7] HUANG, X., FRIEDL, M.A., "Distance Metric-Based Forest Cover Change Detection Using MODIS Time Series", International Journal of Applied Earth Observation and Geoinformation, 29: 78-92, 2014.
• [8] MA, W., JIAO, L., GONG, M., LI, C., "Image Change Detection Based on An Improved Rough Fuzzy C-Means Clustering Algorithm", International Journal of Machine Learning and Cybernetics, 5(3): 369-377, 2013.
• [9] HE, X., "Change Detection for Map Updating with Classification Posterior Probability of HJ Image and TM Image", Image and Data Fusion (ISIDF), 2011 International Symposium on, pp. 1-3, 2011.
• [10] CELIK, T., "Change Detection in Satellite Images Using a Genetic Algorithm Approach", IEEE Geoscience and Remote Sensing Letters, 7(2): 386-390, 2010.
• [11] CELIK, T., "Multiscale Change Detection in Multitemporal Satellite Images", IEEE Geoscience and Remote Sensing Letters, 6 (4): 820-824, 2009.
• [12] HAO, M., ZHANG, H., SHI, W., DENG, K., "Unsupervised Change Detection Using Fuzzy C-Means and MRF From Remotely Sensed Images", Remote Sensing Letters, 4(12): 1185-1194, 2013.
• [13] GONG, M., ZHOU, Z., MA, J., "Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering", IEEE Transactions on Image Processing, 21(4): 2141-2151, 2012.
• [14] CELIK, T., "Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and K-Means Clustering", IEEE Geoscience and Remote Sensing Letters, 6(4): 772-776, 2009.
• [15] MISHRA, N.S., GHOSH, S., GHOSH, A., "Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images", Applied Soft Computing, 12(8): 2683-2692, 2012.
• [16] GHOSH, A., MISHRA, N. S., GHOSH, S., "Fuzzy Clustering Algorithms for Unsupervised Change Detection in Remote Sensing Images", Information Sciences, 181(4): 699-715, 2011.
• [17] HOU, Y., SUN, X., LUN, X., LAN, J., "Gaussian Mixture Model Segmentation Algorithm for Remote Sensing Image", 2010 International Conference on Machine Vision and Human-machine Interface, pp. 275-278, 2010.
• [18] LIU, X. Y., LIAO, Z. W., WANG, Z. S., CHEN, W. F., "Gaussian Mixture Models Clustering Using Markov Random Field for Multispectral Remote Sensing Images", 2006 International Conference on Machine Learning and Cybernetics, pp. 4155-4159, 2006.
• [19] NEAGOE, V. E., CHIRILA-BERBENTEA, V., "Improved Gaussian Mixture Model with Expectation-Maximization for Clustering of Remote Sensing Imagery", 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3063-3065, 2016.
• [20] ZHAO, B., ZHONG, Y., MA, A., ZHANG, L., "A Spatial Gaussian Mixture Model for Optical Remote Sensing Image Clustering", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9 (12): 5748-5759, 2016.
• [21] REYNOLDS, D.A., QUATIERI, T.F., DUNN, R.B., "Speaker Verification Using Adapted Gaussian Mixture Models", Digital Signal Processing, 10(1): 19-41, 2000.
• [22] ATASEVER, U.H., "A New Unsupervised Change Detection Approach with Hybrid Clustering for Detecting The Areal Damage After Natural Disaster", Fresenius Environmental Bulletin, 26(6): 3891-3896, 2017.
• [23] HORE, A., ZIOU, D., "Image Quality Metrics: PSNR vs. SSIM", 2010 20th International Conference on Pattern Recognition, pp. 2366-2369, 2010.
• [24] ZHOU, W., BOVIK, A.C., SHEIKH, H.R., SIMONCELLI, E.P., "Image quality assessment: from error visibility to structural similarity", IEEE Transactions on Image Processing, 13(4): 600-612, 2004.
• [25] ZHOU, W., BOVIK, A.C., "A Universal Image Quality Index", IEEE Signal Processing Letters, 9 (3): 81-84, 2002.