Laplacian pyramid-based change detection in multitemporal SAR images

References

• Adelson, E.H., Anderson, C.H., Bergen, J.R., Burt, P.J., & Ogden, J.M. (1984). Pyramid methods in image processing. RCA Engineer, 29(6), 33–41. Retrieved from http://persci.mit.edu/pub_pdfs/RCA84.pdf
   Google Scholar
• Ajadi, O.A., Meyer, F.J., & Webley, P.W. (2016). Change detection in synthetic aperture radar images using a multiscale-driven approach. Remote Sensing, 8(6), 1–27, 482. doi:10.3390/rs8060482
   Web of Science ®Google Scholar
• Alexander, D., Joni-Kristian, K., Lensu, L., Vartiainen, J., Heikki, K., & Tuomas, E. (2011). Thresholding-based detection of fine and sparse details. Frontiers of Electrical and Electronics Engineering in China, 6(2), 328–338. doi:10.1007/s11460-011-0139-x
   Google Scholar
• Ashok, S., Varshney, P.K., & Arora, M.K. (2007). Robustness of change detection algorithms in the presence of registration errors. Photogrammetric Engineering and Remote Sensing, 73(4), 375–383. doi:10.14358/PERS.73.4.375
   Web of Science ®Google Scholar
• Ban, Y., & Yousif, O.A. (2012). Multitemporal spaceborne SAR data for urban change detection in china. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(4), 1087–1094. doi:10.1109/JSTARS.2012.2201135
   Web of Science ®Google Scholar
• Baselice, F., Ferraioli, G., & Pascazio, V. (2014). Markovian change detection of urban areas using very high resolution complex SAR images. IEEE Geoscience and Remote Sensing Letters, 11(5), 995–999. doi:10.1109/LGRS.2013.2284297
   Web of Science ®Google Scholar
• Bazi, Y., Bruzzone, L., & Melgani, F. (2005). An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing, 43(4), 874–887. doi:10.1109/TGRS.2004.842441
   Web of Science ®Google Scholar
• Bovolo, F., & Bruzzone, L. (2005). A detail-preserving scale-driven approach to change detection in multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing, 43(12), 2963–2972. doi:10.1109/TGRS.2005.857987
   Web of Science ®Google Scholar
• Bovolo, F., Camps-Valls, G., & Bruzzone, L. (2010). A support vector domain method for change detection in multitemporal images. Pattern Recognition Letters, 31(10), 1148–1154. doi:10.1016/j.patrec.2009.07.002
   Web of Science ®Google Scholar
• Bruzzone, L., & Prieto, D.F. (2000). Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing, 38(3), 1171–1182. doi:10.1109/36.843009
   Web of Science ®Google Scholar
• Burt, P., & Adelson, E. (1983a). Multiresolution spline with application to image mosaics. ACM Transactions on Graphics, 2, 217–236.
   Web of Science ®Google Scholar
• Burt, P.J., & Adelson, E.H. (1983b). The Laplacian pyramid as a compact image code. IEEE Transactions on Communications, 31(4), 532–540. doi:10.1109/TCOM.1983.1095851
   Web of Science ®Google Scholar
• Celik, T. (2009a). Multiscale change detection in multitemporal satellite images. IEEE Geoscience and Remote Sensing Letters, 6(4), 820–824. doi:10.1109/LGRS.2009.2026188
   Web of Science ®Google Scholar
• Celik, T. (2009b). Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geoscience and Remote Sensing Letters, 6(4), 772–776. doi:10.1109/LGRS.2009.2025059
   Web of Science ®Google Scholar
• Celik, T. (2010a). A Bayesian approach to unsupervised multiscale change detection in synthetic aperture radar images. Signal Processing, 90(5), 1471–1485. doi:10.1016/j.sigpro.2009.10.018
   Web of Science ®Google Scholar
• Celik, T. (2010b). A Bayesian approach to unsupervised multiscale change detection in synthetic aperture radar images. Signal Processing, 90(5), 1471–1485. doi:10.1016/j.sigpro.2009.10.018
   Web of Science ®Google Scholar
• Chesnokova, O., & Erten, E. (2013). A comparison between coherent and incoherent similarity measures in terms of crop inventory. IEEE Geoscience and Remote Sensing Letters, 10(2), 303–307. doi:10.1109/lgrs.2012.2203783
   Web of Science ®Google Scholar
• Coupe, P., Hellier, P., Kervrann, C., & Barillot, C. (2009). Nonlocal means-based speckle filtering for ultrasound images. IEEE Transactions on Image Processing, 18(10), 2221–2229. doi:10.1109/TIP.2009.2024064
   PubMed Web of Science ®Google Scholar
• Dekker, R.J. (1998). Speckle filtering in satellite SAR change detection imagery. International Journal of Remote Sensing, 19(6), 1133−1146. doi:10.1080/014311698215649
   Web of Science ®Google Scholar
• Deng-Yuan, H., Ta-Wei, L., & Wu-Chih, H. (2011). Automatic multilevel thresholding based on two-stage Otsu’s method with cluster determination by valley estimation. International Journal of Innovative Computing, Information and Control, 7(10), 5631–5644. http://www.ijicic.org/ijicic-10-05033.pdf
   Web of Science ®Google Scholar
• Dogan, O., & Perissin, D. (2014). Detection of multitransition abrupt changes in multitemporal SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8), 3239–3247. doi:10.1109/JSTARS.2013.2295357
   Web of Science ®Google Scholar
• Fan, J.-L., & Lei, B. (2012). A modified valley-emphasis method for automatic thresholding. Pattern Recognition Letters, 33(6), 703–708. doi:10.1016/j.patrec.2011.12.009
   Web of Science ®Google Scholar
• Feddern, C., Weickert, J., Burgeth, B., & Welk, M. (2006). Curvature-driven PDE methods for matrix-valued images. International Journal of Computer Vision, 69(1), 93–107. doi:10.1007/s11263-006-6854-8
   Web of Science ®Google Scholar
• Frost, V.S., Stiles, J.A., Shanmugan, K.S., & Holtzman, J.C. (1982). A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-4(2), 157−166. doi:10.1109/TPAMI.1982.4767223
   Web of Science ®Google Scholar
• Ghosh, M.K., Kumar, L., & Roy, C. (2015). Monitoring the coastline change of Hatiya Island in Bangladesh using remote sensing techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 137−144. doi:10.1016/j.isprsjprs.2014.12.009
   Web of Science ®Google Scholar
• Gong, M., Cao, Y., & Wu., Q. (2012). A neighborhood-based ratio approach for change detection in SAR images. IEEE Geoscience and Remote Sensing Letters, 9(2), 307–311. doi:10.1109/LGRS.2011.2167211
   Web of Science ®Google Scholar
• Gong, M., Yu, L., Jiao, L., Jia, M., & Su., L. (2014). SAR change detection based on intensity and texture changes. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 123–135. doi:10.1016/j.isprsjprs.2014.04.010
   Web of Science ®Google Scholar
• Gong, M., Zhou, Z., & Jingjing, M. (2012). Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Transactions on Image Processing, 21(4), 2141–2151. doi:10.1109/TIP.2011.2170702
   PubMed Web of Science ®Google Scholar
• Hadjimitsis, D.G., Papadavid, G., Agapiou, A., Themistocleous, K., Hadjimitsis, M.G., Retalis, A., … Clayton, C.R.I. (2010). Atmospheric correction for satellite remotely sensed data intended for agricultural applications: Impact on vegetation indices. Natural Hazards and Earth System Sciences, 10, 89–95. doi:10.5194/nhess-10-89-2010
   Web of Science ®Google Scholar
• Hao, M., Shi, W., Zhang, H., & Li., C. (2014). Unsupervised change detection with expectation-maximization-based level set. IEEE Geoscience and Remote Sensing Letters, 11(1), 210–214. doi:10.1109/lgrs.2013.2252879
   Web of Science ®Google Scholar
• Hazarika, N., Apurba, K.D., & Borah, S.B. (2015). Assessing land-use changes driven by river dynamics in chronically flood affected upper Brahmaputra plains, India, using RS-GIS techniques. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 107−118. doi:10.1016/j.ejrs.2015.02.001
   Web of Science ®Google Scholar
• Heeger, D.J., & Bergen, J.R. (1995). Pyramid-based texture analysis/synthesis. Proceedings, International Conference on Image Processing (pp. 648–651). doi:10.1109/ICIP.1995.537718
   Google Scholar
• Hu, H., & Ban, Y. (2014). Unsupervised change detection in multitemporal SAR images over large urban areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8), 3248–3261. doi:10.1109/JSTARS.2014.2344017
   Web of Science ®Google Scholar
• Inglada, J., & Mercier, G. (2007). A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis. IEEE Transactions on Geoscience and Remote Sensing, 45(5), 1432–1445. doi:10.1109/TGRS.2007.893568
   Web of Science ®Google Scholar
• Jesus, A.-G., Arie, C.S., & Joost, F.D. (2012). Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico. Applied Geography, 34, 29–37. doi:10.1016/j.apgeog.2011.10.010
   Web of Science ®Google Scholar
• John, B.C., & Woodcock, C.E. (1996). An assessment of several linear change detection techniques for mapping forest mortality using multitemporal landsat TM data. Remote Sensing of Environment, 56(1), 66–77. doi:10.1016/0034-4257(95)00233-2
   Web of Science ®Google Scholar
• Kalaivani, S., & Wahidabanu, R.S.D. (2012a). Condensed anisotropic diffusion for speckle reducton and enhancement in ultrasonography. EURASIP Journal on Image and Video Processing, Springer, 1–17. doi:10.1186/1687-5281-2012-12
   Google Scholar
• Kalaivani, S., & Wahidabanu, R.S.D. (2012b). Diagnostic ultrasound image enhancement: A multiscale permutation approach. Journal of Imaging Science and Technology, 56(1), 10501(1–12). doi:10.2352/J.ImagingSci.Technol.2012.56.1.010501
   Web of Science ®Google Scholar
• Kapur, J.N., Sahoo, P.K., & Wong, A.K.C. (1985). A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics, and Image Processing, 29(3), 273–285. doi:10.1016/0734-189X(85)90125-2
   Google Scholar
• Kennedy, R.E., Cohen, W.B., & Schroeder, T.A. (2007). Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment, 110(3), 370–386. doi:10.1016/j.rse.2007.03.010
   Web of Science ®Google Scholar
• Kittler, J., & Illingworth, J. (1986). Minimum error thresholding. Pattern Recognition, 19(1), 41–47. doi:10.1016/0031-3203(86)90030-0
   Web of Science ®Google Scholar
• Krissian, K., Westin, C.-F., Kikinis, R., & Vosburgh, K.G. (2007). Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing, 16(5), 1412–1424. doi:10.1109/TIP.2007.891803
   PubMed Web of Science ®Google Scholar
• Kuan, D.T., Sawchuk, A.A., Strand, T.C., & Chavel, P. (1985). Adaptive noise smoothing filter for images with signal-dependent noise. Ieee Transactions on Pattern Analysis and Machine Intelligence, 7(2), 165−177. doi: 10.1109/tpami.1985.4767641
   PubMed Web of Science ®Google Scholar
• Kuan, D.T., Sawchuk, A.A., Strand, T.C., & Chavel, P. (1987). Adaptive restoration of images with speckle. IEEE Transactions on Acoustics, Speech, and Signal Processing, 35(3), 373−383. doi:10.1109/tassp.1987.1165131
   Google Scholar
• Lal, A.M., & Margret Anouncia, S. (2015). Semi-supervised change detection approach combining sparse fusion and constrained k means for multi-temporal remote sensing images. The Egyptian Journal of Remote Sensing and Space Science, 18(2), 279–288. doi:10.1016/j.ejrs.2015.10.002
   Web of Science ®Google Scholar
• Lee, J.-S. (1980). Digital image enhancement and noise filtering by use of local statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-2(2), 165−168. doi:10.1109/TPAMI.1980.4766994
   Web of Science ®Google Scholar
• Li, X., & Yeh, A.G.O. (1998). Principal component analysis of stacked multi-temporal images for the monitoring of rapid urban expansion in the Pearl River Delta. International Journal of Remote Sensing, 19(8), 1501–1518. doi:10.1080/014311698215315
   Web of Science ®Google Scholar
• Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365−2407. doi:10.1080/0143116031000139863
   Web of Science ®Google Scholar
• Ma, J., Gong, M., & Zhou, Z. (2012). Wavelet fusion on ratio images for change detection in SAR images. IEEE Geoscience and Remote Sensing Letters, 9(6), 1122–1126. doi:10.1109/LGRS.2012.2191387
   Web of Science ®Google Scholar
• Masoomi, A., Hamzehyan, R., & Shirazi, N.C. (2012). Speckle reduction approach for SAR image in satellite communication. International Journal of Machine Learning and Computing, 2(1), 62–70. doi:10.1109/IGARSS.2017.8127463
   Google Scholar
• Masroor, H., Dongmei, C., Angela, C., Hui, W., & David, S. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, 91–106. doi:10.1016/j.isprsjprs.2013.03.006
   Web of Science ®Google Scholar
• Mehmet, S., & Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13(1), 146–165. doi:10.1117/1.1631315
   Web of Science ®Google Scholar
• Michael, U. (1992). An improved least squares laplacian pyramid for image compression. Signal Processing, 27(2), 187–203. doi:10.1016/0165-1684(92)90007-J
   Web of Science ®Google Scholar
• Michailovich, O.V., & Tannenbaum, A. (2006). Despeckling of medical ultrasound images. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 53(1), 64–78. doi:10.1109/TUFFC.2006.1588392
   PubMed Web of Science ®Google Scholar
• Moser, G., & Serpico, S.B. (2006). Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(10), 2972–2982. doi:10.1109/TGRS.2006.876288
   Web of Science ®Google Scholar
• Mura, M.D., Benediktsson, J.A., Bovolo, F., & Bruzzone, L. (2008). An unsupervised technique based on morphological filters for change detection in very high resolution images. IEEE Geoscience and Remote Sensing Letters, 5(3), 433–437. doi:10.1109/LGRS.2008.917726
   Web of Science ®Google Scholar
• Muthukumaran, M., Gopalakrishnan, S., Purna, C., Soumitra, K., & Saravanan, T. (2017). An improved version of Otsu’s method for segmentation of weld defects on X-radiography images. Optik, 142, 109–118. doi:10.1016/j.ijleo.2017.05.066
   Web of Science ®Google Scholar
• Otsu, N. (1979). A Threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. doi:10.1109/TSMC.1979.4310076
   Web of Science ®Google Scholar
• Patel, V.M., Easley, G.R., Chellappa, R., & Nasrabadi, N.M. (2014). Separated component-based restoration of speckled SAR images. IEEE Transactions on Geoscience and Remote Sensing, 52(2), 1019–1029. doi:10.1109/TGRS.2013.2246794
   Web of Science ®Google Scholar
• Perona, P., & Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7), 629–639. doi:10.1109/34.56205
   Web of Science ®Google Scholar
• Perry, J. (1997). Image compression using Laplacian pyramid encoding. C/C++ Users Journal, 15(2), 35–47.
   Google Scholar
• Pratt, W.K. (1978). Digital image processing. A Wiley-Interscience Publication, John Wiley & Sons, Inc. Retrieved from https://dl.acm.org/citation.cfm?id=108781.
   Google Scholar
• Radke, R.J., Andra, S., Al-kofahi, O., & Roysam, B. (2005). Image change detection algorithms: A systematic survey. IEEE Transactions on Image Processing, 14(3), 294–307. doi:10.1109/TIP.2004.838698
   PubMed Web of Science ®Google Scholar
• Rajan, J., & Kaimal, R.M. (2006). Speckle reduction in images with WEAD and WECD, Springer ICVGIP, LNCS 4338, 184–193. doi:10.1007/11949619_17
   Google Scholar
• Rosenfield, G.H., & Fitzpatrick-Lins, K. (1986). A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric Engineering and Remote Sensing, 52(2), 223–227.
   Web of Science ®Google Scholar
• Rosin, P.L. (2001). Unimodal thresholding. Pattern Recognition, 34(11), 2083–2096. doi:10.1016/S0031-3203(00)00136-9
   Web of Science ®Google Scholar
• Rosin, P.L., & Ioannidis, E. (2003). Evaluation of global image thresholding for change detection. Pattern Recognition Letters, 24(14), 2345–2356. doi:10.1016/S0167-8655(03)00060-6
   Web of Science ®Google Scholar
• Sartajvir, S., & Rajneesh, T. (2014). A comparative study on change vector analysis based change detection techniques. Sadhana, 39(6), 1311–1331. doi:10.1007/s12046-014-0286-x
   Web of Science ®Google Scholar
• Schmitt, A., Wendleder, A., & Hinz, S. (2015). The Kennaugh element framework for multi-scale, multi-polarized, multi-temporal and multi-frequency SAR image preparation. ISPRS Journal of Photogrammetry and Remote Sensing, 102, 122–139. doi:10.1016/j.isprsjprs.2015.01.007
   Web of Science ®Google Scholar
• Shalaby, A., & Tateishi, R. (2007). Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography, 27(1), 28–41. doi:10.1016/j.apgeog.2006.09.004
   Web of Science ®Google Scholar
• Shang, R., Qi., L., Jiao, L., Stolkin, R., & Li, Y. (2014). Change detection in SAR images by artificial immune multi-objective clustering. Engineering Applications of Artificial Intelligence, 31, 53–67. doi:10.1016/j.engappai.2014.02.004
   Web of Science ®Google Scholar
• Sheng, Y., & Xia, Z.-G. (1996). A comprehensive evaluation of filters for radar speckle suppression. International Geoscience and Remote Sensing Symposium, 1559–1561, doi:10.1109/IGARSS.1996.516730
   Google Scholar
• Singh, A. (1989). Review article digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003. doi:10.1080/01431168908903939
   Web of Science ®Google Scholar
• Tsai, D.-M. (1995). A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recognition Letters, 16(6), 653–666. doi:10.1016/0167-8655(95)80011-H
   Web of Science ®Google Scholar
• Wang, F., Wu, Y., Zhang, Q., Zhang, P., Ming, L., & Yunlong, L. (2013). Unsupervised change detection on SAR images using Triplet Markov field model. IEEE Geoscience and Remote Sensing Letters, 10(4), 697–701. doi:10.1109/LGRS.2012.2219494
   Web of Science ®Google Scholar
• Xu, X., Xu, S., Jin, L., & Song, E. (2011). Characteristic analysis of Otsu threshold and its applications. Pattern Recognition Letters, 32(7), 956–961. doi:10.1016/j.patrec.2011.01.021
   Web of Science ®Google Scholar
• Yang, X., Shen, X., Long, J., & Chen, H. (2012). An improved median-based Otsu image thresholding algorithm. AASRI Procedia, 3, 468–473. doi:10.1016/j.aasri.2012.11.074
   Google Scholar
• you, Y.-L., & Kaveh, M. (2000). Fourth-order partial differential equations for noise removal. IEEE Transactions on Image Processing, 9(10), 1723–1730. doi:10.1109/83.869184
   PubMed Web of Science ®Google Scholar
• yousif, O., & Ban, Y. (2013). Improving urban change detection from multitemporal SAR images using PCA-NLM. IEEE Transactions on Geoscience and Remote Sensing, 51(4), 2032–2041. doi:10.1109/TGRS.2013.2245900
   Web of Science ®Google Scholar
• yousif, O., & Ban, Y. (2014). Improving SAR-based urban change detection by combining MAP-MRF classifier and nonlocal means similarly weights. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10), 4288–4300. doi:10.1109/jstars.2014.2347171
   Web of Science ®Google Scholar
• Yu, S.X. (2009). Edge-preserving Laplacian pyramid. 5th International Symposium on Visual computing, USA, 1–10.
   Google Scholar
• Yu, Y., & Acton, S.T. (2002). Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing, 11(11), 1260–1270. doi:10.1109/TIP.2002.804276
   PubMed Web of Science ®Google Scholar
• Zhang, F., Yoo, Y.M., Koh, L.M., & Kim, Y. (2007). Nonlinear diffusion in Laplacian pyramid domain for ultrasonic speckle reduction. IEEE Transactions on Medical Imaging, 26(2), 200–211. doi:10.1109/TMI.2006.889735
   PubMed Web of Science ®Google Scholar