Hyperspectral image classification using set-to-set distance

Hyperspectral image (HSI) classification has attracted much attention and extensive research efforts over the past decade. Due to few labeled samples versus high dimensional features, it is a challenging problem in practice. Recently, combining the pixel spectral information and the spatial (neighborhood) information has been verified to be effective for HSI classification. In this paper, we introduce a novel method for HSI classification using set-to-set distance (SSD). Based on the assumption that neighbor pixels tend to belong to the same class with high probability, we model a test pixel and its neighbor pixels as a testing set (or a neighbor set) inspired by bilateral filtering. Meanwhile, the training pixels belong to the same class are modeled as a training set. Therefore, the classification is based on comparisons of sets distances. Experiments on a real HSI dataset show that our proposed method outperforms a number of existing state-of-the-art approaches.

[1]  Xiang Ma,et al.  Sparse Support Regression for Image Super-Resolution , 2015, IEEE Photonics Journal.

[2]  Gabriele Moser,et al.  Combining Support Vector Machines and Markov Random Fields in an Integrated Framework for Contextual Image Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Qian Du,et al.  Joint Within-Class Collaborative Representation for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Zhiliang Zhu,et al.  Fast Single Image Super-Resolution via Self-Example Learning and Sparse Representation , 2014, IEEE Transactions on Multimedia.

[5]  Jianzhong Guo,et al.  Hyperspectral image classification using Gradient Local Auto-Correlations , 2015, 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR).

[6]  James E. Fowler,et al.  Hyperspectral Image Classification Using Gaussian Mixture Models and Markov Random Fields , 2014, IEEE Geoscience and Remote Sensing Letters.

[7]  Zhuowen Tu,et al.  Robust Point Matching via Vector Field Consensus , 2014, IEEE Transactions on Image Processing.

[8]  Trac D. Tran,et al.  Hyperspectral Image Classification Using Dictionary-Based Sparse Representation , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Ruimin Hu,et al.  Two-step superresolution approach for surveillance face image through radial basis function-partial least squares regression and locality-induced sparse representation , 2013, J. Electronic Imaging.

[10]  Jon Atli Benediktsson,et al.  SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images , 2010, IEEE Geoscience and Remote Sensing Letters.

[11]  Li Ma,et al.  Local Manifold Learning-Based $k$ -Nearest-Neighbor for Hyperspectral Image Classification , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Gustavo Camps-Valls,et al.  Composite kernels for hyperspectral image classification , 2006, IEEE Geoscience and Remote Sensing Letters.

[13]  Jon Atli Benediktsson,et al.  A new approach for the morphological segmentation of high-resolution satellite imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[14]  Alan L. Yuille,et al.  Non-Rigid Point Set Registration by Preserving Global and Local Structures , 2016, IEEE Transactions on Image Processing.

[15]  Jon Atli Benediktsson,et al.  Advances in Spectral-Spatial Classification of Hyperspectral Images , 2013, Proceedings of the IEEE.

[16]  James E. Fowler,et al.  Nearest Regularized Subspace for Hyperspectral Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Qian Du,et al.  Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Chen Chen,et al.  Spectral–Spatial Preprocessing Using Multihypothesis Prediction for Noise-Robust Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[19]  Jun Zhou,et al.  Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Yan Guo,et al.  Local-Manifold-Learning-Based Graph Construction for Semisupervised Hyperspectral Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Ruimin Hu,et al.  Low-Resolution and Low-Quality Face Super-Resolution in Monitoring Scene via Support-Driven Sparse Coding , 2014, J. Signal Process. Syst..