Spectral-spatial hyperspectral image classification via SVM and superpixel segmentation

Integration of spatial information has recently emerged as a powerful tool in improving the classification accuracy of hyperspectral image (HSI). However, partitioning homogeneous regions of the HSI remains a challenging task. This paper proposes a novel spectral-spatial classification method inspired by the support vector machine (SVM) and superpixel segmentation. Core ideas of the proposed method are twofold: 1) the HSI is first classified by the pixel-wise classifier (i.e. SVM); 2) a fast superpixel segmentation-based spatial processing is, for the first time, introduced in this study to refine the homogeneity and consistency of the classification maps. Experiments are conducted on two benchmark HSIs (i.e. the Indian Pines data and the Washington, D.C. Mall data) with different spectral and spatial resolutions. It is found that the proposed method yields more accurate classification results compared to the state-of-the-art techniques.

[1]  Bor-Chen Kuo,et al.  Feature Mining for Hyperspectral Image Classification , 2013, Proceedings of the IEEE.

[2]  J. Benedetto,et al.  Nonlinear Dimensionality Reduction via the ENH-LTSA Method for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  沈毅,et al.  An optimization-based ensemble EMD for classification of hyperspectral images , 2013 .

[4]  Johannes R. Sveinsson,et al.  Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles , 2008, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[5]  Jon Atli Benediktsson,et al.  Multiple Spectral–Spatial Classification Approach for Hyperspectral Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Wei Xia,et al.  Band Selection for Hyperspectral Imagery: A New Approach Based on Complex Networks , 2013, IEEE Geoscience and Remote Sensing Letters.

[7]  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 .

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9]  Richard J. Duro,et al.  Unmixing Low-Ratio Endmembers in Hyperspectral Images Through Gaussian Synapse ANNs , 2010, IEEE Transactions on Instrumentation and Measurement.

[10]  Jiasong Zhu,et al.  Discriminative Gabor Feature Selection for Hyperspectral Image Classification , 2013, IEEE Geoscience and Remote Sensing Letters.

[11]  Xindong Wu,et al.  A relation extraction method of Chinese named entities based on location and semantic features , 2009, 2009 IEEE International Conference on Granular Computing.

[12]  Peter Bajorski,et al.  Statistical Inference in PCA for Hyperspectral Images , 2011, IEEE Journal of Selected Topics in Signal Processing.

[13]  Richard J. Duro,et al.  An Adaptive Approach for the Progressive Integration of Spatial and Spectral Features When Training Ground-Based Hyperspectral Imaging Classifiers , 2010, IEEE Transactions on Instrumentation and Measurement.

[14]  Jon Atli Benediktsson,et al.  Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis , 2011, IEEE Geoscience and Remote Sensing Letters.

[15]  Yi Shen,et al.  Multivariate Gray Model-Based BEMD for Hyperspectral Image Classification , 2013, IEEE Transactions on Instrumentation and Measurement.

[16]  Richard J. Duro,et al.  Gaussian synapse ANNs in multi- and hyperspectral image data analysis , 2003, IEEE Trans. Instrum. Meas..

[17]  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.

[18]  Jon Atli Benediktsson,et al.  Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas , 2009, EURASIP J. Adv. Signal Process..

[19]  Zheng Tian,et al.  Neighborhood Preserving Orthogonal PNMF Feature Extraction for Hyperspectral Image Classification , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  Lianru Gao,et al.  Adaptive Markov Random Field Approach for Classification of Hyperspectral Imagery , 2011, IEEE Geoscience and Remote Sensing Letters.

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

[22]  Jianbin Qiu,et al.  A novel approach to hyperspectral band selection based on spectral shape similarity analysis and fast branch and bound search , 2014, Eng. Appl. Artif. Intell..

[23]  Begüm Demir,et al.  Empirical Mode Decomposition of Hyperspectral Images for Support Vector Machine Classification , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Ryuei Nishii,et al.  Hyperspectral Image Classification by Bootstrap AdaBoost With Random Decision Stumps , 2007, IEEE Transactions on Geoscience and Remote Sensing.

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

[26]  Liangpei Zhang,et al.  An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery , 2013, IEEE Transactions on Geoscience and Remote Sensing.

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

[28]  Trac D. Tran,et al.  Hyperspectral Image Classification via Kernel Sparse Representation , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Rama Chellappa,et al.  Entropy rate superpixel segmentation , 2011, CVPR 2011.

[30]  Bor-Chen Kuo,et al.  Feature Mining for Hyperspectral Image Classification A general overview and analysis of feature reduction methods for classification of hyperspectral images is provided. Experimental results give the performance of selected feature selection and feature extraction approaches. , 2013 .