Hyperspectral Image Classification Using Principal Components-Based Smooth Ordering and Multiple 1-D Interpolation

This paper proposes a spectral-spatial classification algorithm based on principal components (PCs)-based smooth ordering and multiple 1-D interpolation, which can alleviate the general classification problems effectively. Because of the characteristics of hyperspectral image, there always exist easily separable samples (ESSs) and difficultly separable samples (DSSs) in view of the different sets of labeled samples. In this paper, the PC analysis is first used for reducing features and extracting the few first PCs of a hyperspectral image. Then, PC-based smooth ordering is designed for the separation of ESSs and DSSs, and multiple 1-D interpolation is used for the accurate classification of the ESSs. Next, the highly confident samples are selected from the ESSs by the spatial neighborhood information, which are added into the training set for the classification of DSSs. In the case of sufficient training samples, a supervised spectral-spatial method is used for classifying the DSSs by combining the spatial information built with popular extended multiattribute profiles. The proposed algorithm is compared with some state-of-the-art methods on three hyperspectral data sets. The results demonstrate that the presented algorithm achieves much better classification performance in terms of the accuracy and the computation time.

[1]  Johannes R. Sveinsson,et al.  Automatic Spectral–Spatial Classification Framework Based on Attribute Profiles and Supervised Feature Extraction , 2014, IEEE Transactions on Geoscience and Remote Sensing.

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

[3]  Jon Atli Benediktsson,et al.  A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Jos B. T. M. Roerdink,et al.  Mathematical Morphology and Its Applications to Signal and Image Processing , 2013 .

[5]  Michael Elad,et al.  Image Processing Using Smooth Ordering of its Patches , 2012, IEEE Transactions on Image Processing.

[6]  Jianzhong Wang.  Semi-supervised learning using ensembles of multiple 1D-embedding-based label boosting , 2016, Int. J. Wavelets Multiresolution Inf. Process..

[7]  Jon Atli Benediktsson,et al.  Recent Advances in Techniques for Hyperspectral Image Processing , 2009 .

[8]  Jon Atli Benediktsson,et al.  Automatic Framework for Spectral–Spatial Classification Based on Supervised Feature Extraction and Morphological Attribute Profiles , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[10]  Hong Li,et al.  Hyperspectral Image Classification Using Spectral–Spatial Composite Kernels Discriminant Analysis , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  Lorenzo Bruzzone,et al.  A Novel Transductive SVM for Semisupervised Classification of Remote-Sensing Images , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Johannes R. Sveinsson,et al.  Classification of hyperspectral data from urban areas based on extended morphological profiles , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Jon Atli Benediktsson,et al.  A novel semi-supervised learning framework for hyperspectral image classification , 2016, Int. J. Wavelets Multiresolution Inf. Process..

[14]  Jon Atli Benediktsson,et al.  Classification of hyperspectral data using extended attribute profiles based on supervised and unsupervised feature extraction techniques , 2012 .

[15]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[16]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[17]  Jon Atli Benediktsson,et al.  Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods , 2013, IEEE Signal Processing Magazine.

[18]  Hong Li,et al.  Hyperspectral Image Classification Using Functional Data Analysis , 2014, IEEE Transactions on Cybernetics.

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

[20]  Antonio J. Plaza,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1 Spectral–Spatial Classification of Hyperspectral Data Usi , 2022 .

[21]  Antonio J. Plaza,et al.  Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning , 2010, IEEE Transactions on Geoscience and Remote Sensing.

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

[23]  Lorenzo Bruzzone,et al.  Extended profiles with morphological attribute filters for the analysis of hyperspectral data , 2010 .

[24]  Yalong Song,et al.  Multiple one-dimensional embedding clustering scheme for hyperspectral image classification , 2016, Int. J. Wavelets Multiresolution Inf. Process..

[25]  Thomas S. Huang,et al.  Semisupervised Hyperspectral Classification Using Task-Driven Dictionary Learning With Laplacian Regularization , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Jon Atli Benediktsson,et al.  Morphological Attribute Profiles for the Analysis of Very High Resolution Images , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Jianzhong Wang,et al.  Semi-supervised learning using multiple one-dimensional embedding based adaptive interpolation , 2016, Int. J. Wavelets Multiresolution Inf. Process..

[28]  Jon Atli Benediktsson,et al.  Classification and feature extraction for remote sensing images from urban areas based on morphological transformations , 2003, IEEE Trans. Geosci. Remote. Sens..