A Novel Semicoupled Projective Dictionary Pair Learning Method for PolSAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) image classification plays an important role in remote sensing image processing. In recent years, stacked auto-encoder (SAE) has obtained a series of excellent results in PolSAR image classification. The recently proposed projective dictionary pair learning (DPL) model takes both accuracy and time consumption into consideration, and another recently proposed semicoupled dictionary learning (SCDL) model gives a new way to fit different features. Based on the SAE, DPL, and SCDL models, we propose a novel semicoupled projective DPL method with SAE (SAE-SDPL) for PolSAR image classification. Our method can get the classification result efficiently and correctly and meanwhile giving a new method to fit different features. In this paper, three PolSAR images are used to test the performance of SAE-SDPL. Compared with some state-of-the-art methods, our method obtains excellent results in PolSAR image classification.

[1]  M. Hellmann,et al.  Classification of full polarimetric SAR-data using artificial neural networks and fuzzy algorithms , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[2]  Qi Li,et al.  Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features , 2016, Remote. Sens..

[3]  Jean-Claude Souyris,et al.  Support Vector Machine for Multifrequency SAR Polarimetric Data Classification , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Lawrence Carin,et al.  Sparse multinomial logistic regression: fast algorithms and generalization bounds , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Stephen L. Durden,et al.  A three-component scattering model for polarimetric SAR data , 1998, IEEE Trans. Geosci. Remote. Sens..

[6]  E. Pottier,et al.  On radar polarization target decomposition theorems with application to target classification, by using neural network method , 1991 .

[7]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[8]  Michael Elad,et al.  Dictionaries for Sparse Representation Modeling , 2010, Proceedings of the IEEE.

[9]  Lei Zhang,et al.  Projective dictionary pair learning for pattern classification , 2014, NIPS.

[10]  Jonathan Li,et al.  Learning Hierarchical Features for Automated Extraction of Road Markings From 3-D Mobile LiDAR Point Clouds , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  Shuang Wang,et al.  Multilayer feature learning for polarimetric synthetic radar data classification , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[12]  Larry S. Davis,et al.  Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Gang Wang,et al.  Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[14]  Jakob J. van Zyl,et al.  Model-Based Decomposition of Polarimetric SAR Covariance Matrices Constrained for Nonnegative Eigenvalues , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Quan Pan,et al.  Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Biao Hou,et al.  Classification of Polarimetric SAR Images Using Multilayer Autoencoders and Superpixels , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  Kun-Shan Chen,et al.  Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network , 1996, IEEE Trans. Geosci. Remote. Sens..

[18]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[19]  Peijun Du,et al.  Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features , 2015 .

[20]  Jean Richard Huynen,et al.  Physical reality of radar targets , 1993, Optics & Photonics.

[21]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  L. Novak,et al.  Bayes classification of terrain cover using normalized polarimetric data , 1988 .

[23]  Fang Liu,et al.  POL-SAR Image Classification Based on Wishart DBN and Local Spatial Information , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Dan Zhang,et al.  Stacked Sparse Autoencoder in PolSAR Data Classification Using Local Spatial Information , 2016, IEEE Geoscience and Remote Sensing Letters.

[25]  Lei Wang,et al.  Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Yiming Pi,et al.  Polarimetric Contextual Classification of PolSAR Images Using Sparse Representation and Superpixels , 2014, Remote. Sens..

[27]  E. Krogager.  New decomposition of the radar target scattering matrix , 1990 .

[28]  S. Cloude.  Target decomposition theorems in radar scattering , 1985 .

[29]  Lamei Zhang,et al.  Classification of Polarimetric SAR Image Based on Support Vector Machine Using Multiple-Component Scattering Model and Texture Features , 2010, EURASIP J. Adv. Signal Process..

[30]  Eric Pottier,et al.  Dr. J. R. Huynen's main contributions in the development of polarimetric radar techniques and how the 'Radar Targets Phenomenological Concept' becomes a theory , 1993, Optics & Photonics.

[31]  S. Fukuda,et al.  Support vector machine classification of land cover: application to polarimetric SAR data , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[32]  Jong-Sen Lee,et al.  Polarimetric SAR speckle filtering and its implication for classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[33]  Jun Wu,et al.  A Hierarchical Oil Tank Detector With Deep Surrounding Features for High-Resolution Optical Satellite Imagery , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[34]  Jong-Sen Lee,et al.  Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery , 1994, IEEE Trans. Geosci. Remote. Sens..

[35]  Eric Pottier,et al.  An entropy based classification scheme for land applications of polarimetric SAR , 1997, IEEE Trans. Geosci. Remote. Sens..

[36]  Cheng Wang,et al.  Automated Detection of Three-Dimensional Cars in Mobile Laser Scanning Point Clouds Using DBM-Hough-Forests , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Jorge Nocedal,et al.  Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization , 1997, TOMS.

[38]  Kathrin Klamroth,et al.  Biconvex sets and optimization with biconvex functions: a survey and extensions , 2007, Math. Methods Oper. Res..

[39]  Mingsheng Liao,et al.  A learning-based target decomposition method using Kernel KSVD for polarimetric SAR image classification , 2012, EURASIP J. Adv. Signal Process..

[40]  Hao Chen,et al.  Unsupervised nonparametric classification of polarimetric SAR data using the K-nearest neighbor graph , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[41]  Ron Kwok,et al.  Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution , 1994 .

[42]  Jong-Sen Lee,et al.  The use of fully polarimetric information for the fuzzy neural classification of SAR images , 2003, IEEE Trans. Geosci. Remote. Sens..

[43]  J. Kong,et al.  Identification of Terrain Cover Using the Optimum Polarimetric Classifier , 2012 .

[44]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[45]  W. L. Cameron,et al.  Feature motivated polarization scattering matrix decomposition , 1990, IEEE International Conference on Radar.

[46]  Lamei Zhang,et al.  Fully Polarimetric SAR Image Classification via Sparse Representation and Polarimetric Features , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.