Fusion of texture and wavelet features of PALSAR image using LDA and PCA for land cover classification

References

• Acharya, U. R., et al., 2011. Cost-effective and non-invasive automated benign & malignant thyroid lesion classification in 3D contrast-enhanced ultrasound using combination of wavelets and textures: a class of ThyroScan™ algorithms. Technology in Cancer Research & Treatment, 10 (4), 371–380. doi:10.7785/tcrt.2012.500214
   PubMed Web of Science ®Google Scholar
• Acharya, U. R., et al., 2012. ThyroScreen system: high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform. Computer Methods and Programs in Biomedicine, 107 (2), 233–241. doi:10.1016/j.cmpb.2011.10.001
   PubMed Web of Science ®Google Scholar
• Acharya, U.R., et al., 2016. Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features. Computers in Biology and Medicine, 73, 131–140. doi:10.1016/j.compbiomed.2016.04.009
   PubMed Web of Science ®Google Scholar
• Berberoglu, S., et al., 2010. Utilizing image texture to detect land-cover change in Mediterranean coastal wetlands. International Journal Remote Sensing, 31 (11), 2793–2815. doi:10.1080/01431160903111077
   Web of Science ®Google Scholar
• Chamundeeswari, V., Singh, D., and Singh, K., 2009. An analysis of texture measures in PCA-based unsupervised classification of SAR images. IEEE Geoscience Remote Sensing Letters, 6, 214–218. doi:10.1109/LGRS.2008.2009954
   Web of Science ®Google Scholar
• Chen, F., et al., 2007. SAR images classification using case-based reasoning method. IEEE IGRASS Conference Proceedings, 2048–2051. doi:10.1109/IGARSS.2007.4423234
   Google Scholar
• Chen, Z. and Wang, J., 2007. Muti-polarized sar application to land use and land cover mapping in the mountainous three Sgorges Area, China. Canadian Remote Sensing Society (CRSS)/the American Society for Photogrammetry and Remote Sensing Specialty Conference, October 28 - November 1, Ottawa, Canada.
   Google Scholar
• Cui, M., et al., 2011. Genetic algorithms and linear discriminant analysis based dimensionality reduction for remotely sensed image analysis. IEEE IGRASS Conference Proceedings, 2373–2376. doi:10.1109/IGARSS.2011.6049687
   Google Scholar
• Dahiya, S., Garg, P. K., and Jat, M. K., 2013. A comparative study of various pixel-based image fusion techniques as applied to an urban environment. International Journal of Image and Data Fusion, 4 (3), 197–213. doi:10.1080/19479832.2013.778335
   Google Scholar
• Dekker, R.J., 2003. Texture analysis and classification of ERS SAR images for map updating of urban areas in the Netherlands. IEEE Transactions Geoscience Remote Sensing, 41 (9), 1950–1958. doi:10.1109/TGRS.2003.814628
   Web of Science ®Google Scholar
• Du, Q. and Chang, C.I., 2001. A linear constrained distance-based discriminant analysis for hyperspectral image classification. Pattern Recognition, 34, 361–373. doi:10.1016/S0031-3203(99)00215-0
   Web of Science ®Google Scholar
• Feng, J., Cao, Z., and Pi., Y., 2014. Amplitude and texture feature based SAR image classification with a two-stage approach. IEEE Radar Conference Proceedings, 0360–0364. doi:10.1109/RADAR.2014.6875615
   Google Scholar
• Ferro-Famil, L., Pottier, E., and Lee, J.S., 2001. Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha-Wishart classifier. IEEE Transactions Geoscience Remote Sensing, 39, 2332–2342. doi:10.1109/36.964969
   Web of Science ®Google Scholar
• Gamba, P., 2014. Image and data fusion in remote sensing of urban areas: status issues and research trends. International Journal of Image and Data Fusion, 5, 2–12. doi:10.1080/19479832.2013.848477
   Google Scholar
• Geng, J., et al., 2017. Deep supervised and contractive neural network for SAR image classification. IEEE Transactions Geoscience Remote Sensing, 55, 2442–2459. doi:10.1109/TGRS.2016.2645226
   Web of Science ®Google Scholar
• Gonzalez, R.C. and Woods, R.E., 2013. Digital image processing. Pearson Prentice-Hall. Eleventh impress.
   Google Scholar
• Han, Y. and Shao, Y., 2010. Full polarimetric SAR classification based on Yamaguchi decomposition model and scattering parameters. IEEE International Conference on Progress in Informatics and Computing (PIC), 1104–1108. doi:10.1109/PIC.2010.5687997
   Google Scholar
• Hütt, C., et al., 2016. Best accuracy Land Use/Land Cover (LULC) classification to derive crop types using multitemporal, multisensor, and multi-polarization SAR satellite images. Remote Sensing, 8, 684. doi:10.3390/rs8080684
   Web of Science ®Google Scholar
• Jafari, M., Maghsoudi, Y., and Zoej, M.J.V., 2015. A new method for land cover characterization and classification of polarimetric SAR data using polarimetric signatures. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 3595–3607. doi:10.1109/JSTARS.2014.2387374
   Web of Science ®Google Scholar
• Jin, Y., Ruliang, Y., and Ruohong, H., 2006. Pixel level fusion for multiple SAR images using PCA and wavelet transform. In Proceedings CIE International Conference on Radar, 1–4. doi:10.1109/ICR.2006.343209
   Google Scholar
• Johnson, K.J. and Synovec, R.E., 2002. Pattern recognition of jet fuels: comprehensive GC× GC with ANOVA-based feature selection and principal component analysis. Chemometrics and Intelligent Laboratory Systems, 60, 225–237. doi:10.1016/S0169-7439(01)00198-8
   Web of Science ®Google Scholar
• Kandaswamy, U., Adjeroh, D., and Lee, M.C., 2005. Efficient texture analysis of SAR imagery. IEEE Transactions Geosci Remote Sens, 43, 2075–2083. doi:10.1109/TGRS.2005.852768
   Web of Science ®Google Scholar
• Karthikeyan, T. and Manikandaprabhu, P., 2014. A study on discrete wavelet transform based texture feature extraction for image mining. International Journal of Computer Technology and Applications, 5:1805–1811. http://www.ijcta.com/documents/volumes/vol5issue5/ijcta2014050537.pdf
   Google Scholar
• Kim, T.K., et al., 2005. Component-based LDA face description for image retrieval and MPEG-7 standardisation. Image and Vision Computing, 23, 631–642. doi:10.1016/j.imavis.2005.02.005
   Web of Science ®Google Scholar
• Kociołek, M., et al. 2001. Discrete wavelet transform-derived features for digital image texture analysis. International Conference on Signals and Electronic Systems, 99–104. http://eletel.eu/programy/cost/pdf_9.pdf
   Google Scholar
• Kourgli, A., et al., 2012. Texture modelling for land cover classification of fully polarimetric SAR images. International Journal of Image and Data Fusion, 3, 129–148. doi:10.1080/19479832.2010.551521
   Google Scholar
• Kurosu, T., et al., 2001. Land use classification with textural analysis and the aggregation technique using multi-temporal JERS-1 L-band SAR images. International Journal Remote Sensing, 22, 595–613. doi:10.1080/01431160050505874
   Web of Science ®Google Scholar
• Lee, J.S., Grunes, M.R., and Pottier, E., 2001. Quantitative comparison of classification capability: fully polarimetric versus dual and single-polarization SAR. IEEE Transactions Geoscience Remote Sensing, 39, 2343–2351. doi:10.1109/36.964970
   Web of Science ®Google Scholar
• Li, M. and Yuan, B., 2005. 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recognit Letters, 26, 527–532. doi:10.1016/j.patrec.2004.09.007
   Web of Science ®Google Scholar
• Liu, P., et al., 2010. Identification of ocean oil spills in SAR imagery based on fuzzy logic algorithm. International Journal Remote Sensing, 31, 4819–4833. doi:10.1080/01431161.2010.485147
   Web of Science ®Google Scholar
• Mahmoud, A., et al., 2011. Field-based landcover classification using TerraSAR-X texture analysis. Advances in Space Research, 48, 799–805. doi:10.1016/j.asr.2011.04.005
   Web of Science ®Google Scholar
• Martínez, A.M. and Kak, A.C., 2001. Pca versus lda. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (2), 228–233. doi:10.1109/34.908974
   Web of Science ®Google Scholar
• Masjedi, A., Zoej, M.J.V., and Maghsoudi, Y., 2016. Classification of polarimetric SAR images based on modeling contextual information and using texture features. IEEE Transactions on Geoscience and Remote Sensing, 54, 932–943. doi:10.1109/TGRS.2015.2469691
   Web of Science ®Google Scholar
• Miranda, F.P., Fonseca, L.E.N., and Carr, J.R., 1998. Semivariogram textural classification of JERS-1 (Fuyo-1) SAR data obtained over a flooded area of the Amazon rainforest. International Journal Remote Sensing, 19, 549–556. doi:10.1080/014311698216170
   Web of Science ®Google Scholar
• Mishra, P., Singh, D., and Yamaguchi, Y., 2011. Land cover classification of PALSAR images by knowledge based decision tree classifier and supervised classifiers based on SAR observables. Progress In Electromagnetics Research B, 30, 47–70. doi:10.2528/PIERB11011405
   Google Scholar
• Mittal, V., Singh, D., and Saini, L.M., 2015. A critical analysis of EM based fusion of different polarization data for effect on land cover classification. Advances in Space Research, 56, 1094–1105. doi:10.1016/j.asr.2015.06.004
   Web of Science ®Google Scholar
• Myint, S.W., 2001. A robust texture analysis and classification approach for urban land use and land cover feature discrimination. Geocarto International, 16, 29–40. doi:10.1080/10106040108542212
   Google Scholar
• Nava, R., Cristóbal, G., and Escalante-Ramírez, B., 2012. A comprehensive study of texture analysis based on local binary patterns. SPIE Photonics Europe, International Society for Optics and Photonics, 8436, 84360E–84360E. doi:10.1117/12.923558
   Google Scholar
• Novak, L.M., Owirka, G.J., and Netishen, C.M., 1996. Performance of a high-resolution polarimetric SAR automatic target recognition system. Lincoln Laboratory Journal, 6, 11–23.
   Google Scholar
• Otukei, J.R., Blaschke, T., and Collins, M., 2014. Using the Wishart maximum likelihood classifier for assessing the potential of TerraSAR-X and ALOS PALSAR data for land cover mapping. International Journal of Image and Data Fusion, 5, 138–151. doi:10.1080/19479832.2013.870933
   Google Scholar
• Pal, S.K., Majumdar, T.J., and Bhattacharya, A.K., 2007. ERS-2 SAR and IRS-1C LISS III data fusion: a PCA approach to improve remote sensing based geological interpretation. ISPRS Journal of Photogrammetry and Remote Sensing, 61, 281–297. doi:10.1016/j.isprsjprs.2006.10.001
   Web of Science ®Google Scholar
• Pingxiang, L. and Shenghui, F., 2003. SAR image classification based on its texture features. Geo-Spatial Information Science, 6, 16–19. doi:10.1007/BF02826887
   Google Scholar
• Qiu, C., et al., 2009. Performance comparison of target classification in SAR images based on PCA and 2D-PCA features. APSAR Conference Proceedings, 868–871. doi:10.1109/APSAR.2009.5374193
   Google Scholar
• Rakwatin, P., et al., 2012. Using multiscale texture information from ALOS PALSAR to map tropical forest. International Journal Remote Sensing, 33 (24), 7727–7746. doi:10.1080/01431161.2012.701349
   Web of Science ®Google Scholar
• Stanković, R.S. and Falkowski, B.J., 2003. The Haar wavelet transform: its status and achievements. Computers & Electrical Engineering, 29, 25–44. doi:10.1016/S0045-7906(01)00011-8
   Web of Science ®Google Scholar
• Su, W., et al., 2012. Analysis of wavelet packet and statistical textures for object-oriented classification of forest-agriculture ecotones using SPOT 5 imagery. International Journal Remote Sensing, 33, 3557–3579. doi:10.1080/01431161.2010.532823
   Web of Science ®Google Scholar
• Tsiaparas, N., et al., 2009. Discrete wavelet transform vs. wavelet packets for texture analysis of ultrasound images of carotid atherosclerosis. IEEE 9th International Conference on Information Technology and Applications in Biomedicine, 1–4. doi:10.1109/ITAB.2009.5394445
   Google Scholar
• Unser, H., 1986. Sum and difference histograms for texture classification. IEEE Transactions Pattern Analysis and Machine Intelligence, 8 (1), 18–125. doi:10.1109/TPAMI.1986.4767760
   Web of Science ®Google Scholar
• Voisin, A., et al., 2013. Classification of very high resolution SAR images of urban areas using copulas and texture in a hierarchical Markov random field model. IEEE Geoscience Remote Sensing Letters, 10, 96–100. doi:10.1109/LGRS.2012.2193869
   Web of Science ®Google Scholar
• Wang, B., et al., 2011. A feature extraction method for synthetic aperture radar (SAR) automatic target recognition based on maximum interclass distance. Science China Technological Sciences, 54, 2520–2524. doi:10.1007/s11431-011-4430-0
   Web of Science ®Google Scholar
• Wang, H., et al., 2016. Pol-SAR classification based on generalized polar decomposition of mueller matrix. IEEE Geoscience Remote Sensing Letters, 13, 565–569. doi:10.1109/LGRS.2016.2525775
   Web of Science ®Google Scholar
• Wang, L.E. and Sousa, W.P., 2009. Distinguishing mangrove species with laboratory measurements of hyperspectral leaf reflectance. International Journal Remote Sensing, 30, 1267–1281. doi:10.1080/01431160802474014
   Web of Science ®Google Scholar
• Wang, S.X. and And He, Z.G., 2008. The fast target recognition approach based on PCA features for SAR images. Journal of National University of Defense Technology, 30 (3), 137–141.
   Google Scholar
• Wood, E.M., et al., 2012. Image texture as a remotely sensed measure of vegetation structure. Remote Sensing of Environment, 121, 516–526. doi:10.1016/j.rse.2012.01.003
   Web of Science ®Google Scholar
• Yonghong, J., 1998. Fusion of Landsat TM and SAR image based on principal component analysis. Remote Sensing Technology and Application, 13, 46–49.
   Google Scholar
• Zhang, C. and Xie, Z., 2012. Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery. Remote Sensing of Environment, 124, 310–320. doi:10.1016/j.rse.2012.05.015
   Web of Science ®Google Scholar
• Zhou, G., et al., 2010. Pol-SAR images classification using texture features and the complex Wishart distribution. IEEE Radar Conference Proceedings, 491–494. doi:10.1109/RADAR.2010.5494572
   Google Scholar