Sparse Robust Filters for scene classification of Synthetic Aperture Radar (SAR) images

With the increasing resolution of Synthetic Aperture Radar (SAR) images, extracting their discriminative features for scenes classification has become a challenging task, because SAR images are very sensitive to target aspect brought by shadowing effects, interaction of the signature with the environment, and so on. Moreover, SAR images are remarkably polluted by the multiplicative speckle noise, which makes the conventional feature extractors inefficient. In this paper we advance new Sparse Robust Filters (SRFs) for automatic learning of discriminant features of scenes. A Hierarchical Group Sparse Coding (HGSC) model is proposed to learn a set of sparse and robust filters, to capture the multiscale local descriptors that are robust to noises. Some experiments are taken on a TerraSAR-X images dataset (in the middle of the Swabian Jura, the Nordlinger Ries, HH, observed on July, 2007), and a Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, to evaluate the performance of our proposed method. The experimental results show that our method can achieve higher classification accuracy compared with other related approaches.

[1]  Jiwen Lu,et al.  Prototype-Based Discriminative Feature Learning for Kinship Verification , 2015, IEEE Transactions on Cybernetics.

[2]  Hong Sun,et al.  Combining pyramid representation and AdaBoost for urban scene classification using high-resolution synthetic aperture radar images , 2011 .

[3]  Zhipeng Liu,et al.  Synthetic aperture radar automatic target recognition using adaptive boosting , 2005, SPIE Defense + Commercial Sensing.

[4]  Xin Niu,et al.  A Novel Contextual Classification Algorithm for Multitemporal Polarimetric SAR Data , 2014, IEEE Geoscience and Remote Sensing Letters.

[5]  M. Nikolova.  An Algorithm for Total Variation Minimization and Applications , 2004 .

[6]  W. Boerner,et al.  Interpretation of the Polarimetric Co-Polarization Phase Tern in Radar Images Obtained with the JPL Airborne L-Band SAR System , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Yoshua Bengio,et al.  Scaling Up Spike-and-Slab Models for Unsupervised Feature Learning , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Feng Zhou,et al.  Tensorial Independent Component Analysis-Based Feature Extraction for Polarimetric SAR Data Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Michael Elad,et al.  Stable recovery of sparse overcomplete representations in the presence of noise , 2006, IEEE Transactions on Information Theory.

[11]  Yan Liu,et al.  A Unified Framework of Latent Feature Learning in Social Media , 2014, IEEE Transactions on Multimedia.

[12]  Yasser Maghsoudi,et al.  Radarsat-2 Polarimetric SAR Data for Boreal Forest Classification Using SVM and a Wrapper Feature Selector , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Edmund T. Rolls,et al.  What determines the capacity of autoassociative memories in the brain? Network , 1991 .

[15]  D. Tolhurst,et al.  Characterizing the sparseness of neural codes , 2001 .

[16]  Dong Chen,et al.  Particle Filter Sample Texton Feature for SAR Image Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[17]  Lei Guo,et al.  Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Chao Lu,et al.  Automatic Target Classification — Experiments on the MSTAR SAR Images , 2005, Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Network.

[19]  Luciano Alparone,et al.  SAR Image Classification Through Information-Theoretic Textural Features, MRF Segmentation, and Object-Oriented Learning Vector Quantization , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  Philippe Réfrégier,et al.  Hierarchical Feature-Based Classification Approach for Fast and User-Interactive SAR Image Interpretation , 2009, IEEE Geoscience and Remote Sensing Letters.

[21]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[22]  Honglak Lee,et al.  Unsupervised feature learning for audio classification using convolutional deep belief networks , 2009, NIPS.

[23]  Honglak Lee,et al.  An Analysis of Single-Layer Networks in Unsupervised Feature Learning , 2011, AISTATS.

[24]  Serkan Kiranyaz,et al.  Integrating Color Features in Polarimetric SAR Image Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Ling Shao,et al.  Feature Learning for Image Classification Via Multiobjective Genetic Programming , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Seisuke Fukuda,et al.  A wavelet-based texture feature set applied to classification of multifrequency polarimetric SAR images , 1999, IEEE Trans. Geosci. Remote. Sens..

[27]  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.

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

[29]  Rongrong Ji,et al.  Learning High-Level Feature by Deep Belief Networks for 3-D Model Retrieval and Recognition , 2014, IEEE Transactions on Multimedia.

[30]  Michael A. Saville,et al.  Rethinking vehicle classification with wide-angle polarimetric SAR , 2014, IEEE Aerospace and Electronic Systems Magazine.

[31]  Wenxian Yu,et al.  SAR Image Classification Based on CRFs With Integration of Local Label Context and Pairwise Label Compatibility , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[32]  Avik Bhattacharya,et al.  A Generic Land-Cover Classification Framework for Polarimetric SAR Images Using the Optimum Touzi Decomposition Parameter Subset—An Insight on Mutual Information-Based Feature Selection Techniques , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[33]  L. Ferro-Famil,et al.  Unsupervised classification and analysis of natural scenes from polarimetric interferometric SAR data , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[34]  Isabelle Bloch,et al.  A first step toward automatic interpretation of SAR images using evidential fusion of several structure detectors , 1999, IEEE Trans. Geosci. Remote. Sens..

[35]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[36]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[37]  Z. Jane Wang,et al.  An Unsupervised Hierarchical Feature Learning Framework for One-Shot Image Recognition , 2013, IEEE Transactions on Multimedia.

[38]  Bo Du,et al.  Saliency-Guided Unsupervised Feature Learning for Scene Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[40]  Tao Tang,et al.  Superpixel Generating Algorithm Based on Pixel Intensity and Location Similarity for SAR Image Classification , 2013, IEEE Geoscience and Remote Sensing Letters.

[41]  Wenxian Yu,et al.  Scene scattering descriptor for urban classification in very high resolution SAR images , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[42]  Gabriele Moser,et al.  Classification of Very High Resolution SAR Images of Urban Areas Using Copulas and Texture in a Hierarchical Markov Random Field Model , 2013, IEEE Geoscience and Remote Sensing Letters.

[43]  Simon J. Doran,et al.  Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  Honglak Lee,et al.  Sparse deep belief net model for visual area V2 , 2007, NIPS.

[45]  Jon Atli Benediktsson,et al.  Multiple Feature Learning for Hyperspectral Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[46]  Ilkay Ulusoy,et al.  Local Primitive Pattern for the Classification of SAR Images , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[47]  Hui Lin,et al.  Impacts of Feature Normalization on Optical and SAR Data Fusion for Land Use/Land Cover Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[48]  Tao Mei,et al.  A Bag-of-Importance Model With Locality-Constrained Coding Based Feature Learning for Video Summarization , 2014, IEEE Transactions on Multimedia.

[49]  Zhouyu Fu,et al.  Discriminant Absorption-Feature Learning for Material Classification , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[50]  Gang Wang,et al.  Learning Discriminative Hierarchical Features for Object Recognition , 2014, IEEE Signal Processing Letters.

[51]  Uwe Stilla,et al.  Model-Based Interpretation of High-Resolution SAR Images of Buildings , 2008, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.