PolSAR image classification based on sparse autoencoder and boundary-preserved WMRF

ZHANG Shu-Yin; HOU Biao; JIAO Li-Cheng; WU Qian

doi:10.11972/j.issn.1001-9014.2018.02.009

[1] Tang X, Jiao L, Emery W J. SAR image content retrieval based on fuzzy similarity and relevance feedback [J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2017, 99:1-19.

[2] Tang X, Jiao L. Fusion similarity-based reranking for SAR image retrieval [J]. IEEE Geoscience & Remote Sensing Letters, 2017, 14(2):242-246.

[3] Ulaby F T, Elachi C. Radar polaritnetry for geoscience applications [J]. Geocarto International, 1990, 5(5):38-38.

[4] Toan T L, Quegan S, Ferro L. Agriculture classification using POLSAR data [J]. 2005, 586(10):32.

[5] Zribi M, Baghdadi N, Holah N, et al. New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion [J]. Remote Sensing of Environment, 2005, 96(3):485-496.

[6] Zhang S, Wang S, Jiao L C, et al. A novel hybrid Freeman/eigenvalue decomposition with general scattering models [J]. Journal of Infrared & Millimeter Waves, 2015, 34(3):265-270.

[7] Lim H H, Swartz A A, Yueh H A, et al. Classification of Earth terrain using polarimetric synthetic aperture radar images [J]. Journal of Geophysical Research Atmospheres, 1989, 94(B6):7049-7057.

[8] Chen C T, Chen K S, Lee J S. The use of fully polarimetric information for the fuzzy neural classification of SAR images [J]. IEEE Transactions on Geoscience & Remote Sensing, 2003, 41(9):2089-2100.

[9] Liu F, Jiao L, Hou B, et al. POL-SAR image classification based on Wishart DBN and local spatial information [J]. IEEE Transactions on Geoscience & Remote Sensing, 2016, 54(6):3292-3308.

[10] Zhang H, Shan Z, Wang C, et al. Polarimetric SAR image classification based on SVM classifier using Radarsat-2 Quad-Pol data[C]// ESA Special Publication. ESA Special Publication, 2013.

[11] Bentes C, Velotto D, Lehner S. Target classification in oceanographic SAR images with deep neural networks: Architecture and initial results[C]// Geoscience and Remote Sensing Symposium. IEEE, 2015:3703-3706.

[12] G.E.Hinton, R. Salakhutdinov. Reducing the dimensionality of data with neural networks[J]. Science, 2006,313(504): 504-507.

[13] Hou B, Kou H, Jiao L. Classification of Polarimetric SAR Images Using Multilayer Autoencoders and Superpixels [J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2016, 9(7):3072-3081.

[14] Wang Y, Wang G, Lan Y. PolSAR image classification based on deep convolutional neural network [J]. Metallurgical & Mining Industry, 2015.

[15] Xie W, Jiao L, Hou B, et al. POLSAR Image Classification via Wishart-AE Model or Wishart-CAE Model [J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2017, PP (99):1-12.

[16] Zhang L, Ma W, Zhang D. Stacked Sparse Autoencoder in PolSAR Data Classification Using Local Spatial Information [J]. IEEE Geoscience & Remote Sensing Letters, 2016, 13(9):1359-1363.

[17] A. Ng, “Sparse autoencoder,” CS294A Lecture Notes, 2011, 72：1-19.

[18] Wu Y, Ji K, Yu W, et al. Region-based classification of polarimetric SAR images using Wishart MRF [J]. IEEE Geoscience & Remote Sensing Letters, 2008, 5(4):668-672.

[19] Chen C T, Chen K S, Lee J S. The use of fully polarimetric information for the fuzzy neural classification of SAR images [J]. IEEE Transactions on Geoscience & Remote Sensing, 2003, 41(9):2089-2100.

[20] Hosseiniasl E, Zurada J M, Nasraoui O. Deep learning of part-based representation of data using sparse autoencoders with nonnegativity constraints.[J]. IEEE Transactions on Neural Networks & Learning Systems, 2016, 27(12):2486-2498.

[21] Lardeux C, Frison P L, Tison C, et al. Support vector machine for multifrequency SAR polarimetric data classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 2009, 47(12):4143-4152.