Self-Paced Learning-Based Probability Subspace Projection for Hyperspectral Image Classification

In this paper a self-paced learning-based probability subspace projection (SL-PSP) method is proposed for hyperspectral image classification. First, a probability label is assigned for each pixel, and a risk is assigned for each labeled pixel. Then, two regularizers are developed from a self-paced maximum margin and a probability label graph, respectively. The first regularizer can increase the discriminant ability of features by gradually involving the most confident pixels into the projection to simultaneously push away heterogeneous neighbors and pull inhomogeneous neighbors. The second regularizer adopts a relaxed clustering assumption to make avail of unlabeled samples, thus accurately revealing the affinity between mixed pixels and achieving accurate classification with very few labeled samples. Several hyperspectral data sets are used to verify the effectiveness of SL-PSP, and the experimental results show that it can achieve the state-of-the-art results in terms of accuracy and stability.

[1]  Shuyuan Yang,et al.  Fuzzy Signature-Based Discriminative Subspace Projection for Hyperspectral Data Classification , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  Daphne Koller,et al.  Self-Paced Learning for Latent Variable Models , 2010, NIPS.

[3]  Yang Gao,et al.  Dimensionality Reduction for Hyperspectral Data Based on Class-Aware Tensor Neighborhood Graph and Patch Alignment , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Qinghua Zheng,et al.  Simple to Complex Cross-modal Learning to Rank , 2017, Comput. Vis. Image Underst..

[5]  Chein-I Chang,et al.  Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach , 1994, IEEE Trans. Geosci. Remote. Sens..

[6]  M. Mendenhall,et al.  Relevance-Based Feature Extraction for Hyperspectral Images , 2008, IEEE Transactions on Neural Networks.

[7]  Chengzhi Deng,et al.  Orthogonal Nonnegative Matrix Factorization Combining Multiple Features for Spectral–Spatial Dimensionality Reduction of Hyperspectral Imagery , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Bin Li,et al.  Semisupervised Dual-Geometric Subspace Projection for Dimensionality Reduction of Hyperspectral Image Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Qi Wang,et al.  Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization , 2016, IEEE Transactions on Cybernetics.

[10]  Wei Xiong,et al.  Maximum Orthogonal Subspace Projection Approach to Estimating the Number of Spectral Signal Sources in Hyperspectral Imagery , 2010, IEEE Journal of Selected Topics in Signal Processing.

[11]  Xiaojun Chang,et al.  Feature Interaction Augmented Sparse Learning for Fast Kinect Motion Detection , 2017, IEEE Transactions on Image Processing.

[12]  Xiangrong Zhang,et al.  Semisupervised Dimensionality Reduction of Hyperspectral Images via Local Scaling Cut Criterion , 2013, IEEE Geoscience and Remote Sensing Letters.

[13]  Daoqiang Zhang,et al.  marginFace: A novel face recognition method by average neighborhood margin maximization , 2009, Pattern Recognit..

[14]  Deniz Erdogmus,et al.  RKHS Bayes Discriminant: A Subspace Constrained Nonlinear Feature Projection for Signal Detection , 2009, IEEE Transactions on Neural Networks.

[15]  Lorenzo Bruzzone,et al.  Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images , 2017, IEEE Transactions on Image Processing.

[16]  Erzsébet Merényi,et al.  Classification of hyperspectral imagery with neural networks: comparison to conventional tools , 2014, EURASIP Journal on Advances in Signal Processing.

[17]  Qi Wang,et al.  Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Zhu Ming-han,et al.  Fisher linear discriminant analysis algorithm based on vector muster , 2011 .

[19]  Chein-I Chang,et al.  Constrained band selection for hyperspectral imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Tao Jiang,et al.  Efficient and robust feature extraction by maximum margin criterion , 2003, IEEE Transactions on Neural Networks.

[21]  Chein-I Chang,et al.  A noise subspace projection approach to target signature detection and extraction in an unknown background for hyperspectral images , 1998, IEEE Trans. Geosci. Remote. Sens..

[22]  Bo Du,et al.  Dimensionality Reduction and Classification of Hyperspectral Images Using Ensemble Discriminative Local Metric Learning , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Antonio J. Plaza,et al.  Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Mei Yang,et al.  Dimensionality Reduction of Hyperspectral Images With Sparse Discriminant Embedding , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Shigang Wang,et al.  Discriminative Spectral–Spatial Margin-Based Semisupervised Dimensionality Reduction of Hyperspectral Data , 2015, IEEE Geoscience and Remote Sensing Letters.

[26]  Daoqiang Zhang,et al.  Semisupervised Dimensionality Reduction With Pairwise Constraints for Hyperspectral Image Classification , 2011, IEEE Geoscience and Remote Sensing Letters.

[27]  James E. Fowler,et al.  Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Bin Wang,et al.  A Novel Spatial–Spectral Similarity Measure for Dimensionality Reduction and Classification of Hyperspectral Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Mark L. G. Althouse,et al.  Least squares subspace projection approach to mixed pixel classification for hyperspectral images , 1998, IEEE Trans. Geosci. Remote. Sens..

[30]  Jason Weston,et al.  Curriculum learning , 2009, ICML '09.

[31]  Qi Wang,et al.  Dual-Clustering-Based Hyperspectral Band Selection by Contextual Analysis , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Marcos Eduardo Valle,et al.  A Robust Subspace Projection Autoassociative Memory Based on the M-Estimation Method , 2014, IEEE Transactions on Neural Networks and Learning Systems.