Locality constrained low-rank representation for hyperspectral image classification

This paper addresses the problem of hyperspectral image classification with the low-rank representation (LRR) which has been widely applied in computer vision and pattern recognition. As is known, it has been proved to be effective in subspace segmentation under the assumption that all the subspaces are mutually independent. Nevertheless, in practical applications, this assumption could hardly be guaranteed. In this paper, to sidestep this limitation, we simultaneously exploit the spectral similarity and spatial information of pixels to design a local constraint as the regularizer of LRR, which is referred to as the locality constrained LRR (LCLRR). The experimental results on the AVIRIS hyperspectral image confirm the effectiveness of our proposed method.

[1]  Qian Du,et al.  Sparse Graph-Based Discriminant Analysis for Hyperspectral Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Yi Ma,et al.  The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices , 2010, Journal of structural biology.

[4]  Zhixun Su,et al.  Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation , 2011, NIPS.

[5]  Xuelong Li,et al.  Spectral-Spatial Constraint Hyperspectral Image Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Lorenzo Bruzzone,et al.  Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Qian Du,et al.  Sparse Representation-Based Nearest Neighbor Classifiers for Hyperspectral Imagery , 2015, IEEE Geoscience and Remote Sensing Letters.

[8]  Qian Du,et al.  Hyperspectral image classification with low-rank subspace and sparse representation , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[9]  Gustavo Camps-Valls,et al.  Semi-Supervised Graph-Based Hyperspectral Image Classification , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Yongqiang Zhao,et al.  Hyperspectral Image Denoising via Sparse Representation and Low-Rank Constraint , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Yuan Yan Tang,et al.  Hyperspectral Image Classification Based on Regularized Sparse Representation , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  Shuicheng Yan,et al.  Pairwise Sparsity Preserving Embedding for Unsupervised Subspace Learning and Classification , 2013, IEEE Transactions on Image Processing.

[13]  Yong Yu,et al.  Robust Subspace Segmentation by Low-Rank Representation , 2010, ICML.

[14]  Jie Zhang,et al.  Structure-Constrained Low-Rank Representation , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[15]  G. Sapiro,et al.  A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography. , 2013, Journal of structural biology.