Structure Extraction With Total Variation for Hyperspectral Image Classification

This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV). Specifically, a two-scale decomposition-based relative total variation (TSD-RTV) method is presented for the first time to process the information of different scales, such that the structure can be well extracted. Moreover, a new weighted-average fusion method is introduced, which can reduce the dimensionality and also remove the noise due to hyperspectral sensors. The support vector machine (SVM) is applied to HSI classification as a classifier. The experiments are conducted on three real hyperspectral datasets: Indian Pines, Salinas, and Kennedy Space Center. The experimental results show the outstanding performance of the proposed SETV model in terms of classification accuracy and computational efficiency.

[1]  Liangpei Zhang,et al.  On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Jon Atli Benediktsson,et al.  Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Hongyan Zhang.  HYPERSPECTRAL IMAGE DENOISING WITH CUBIC TOTAL VARIATION MODEL , 2012 .

[4]  Gang Wang,et al.  Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  Xuelong Li,et al.  Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Jon Atli Benediktsson,et al.  Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Liangpei Zhang,et al.  Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Shihong Du,et al.  Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Tomio Goto,et al.  Super-resolution System for 4K-HDTV , 2014, 2014 22nd International Conference on Pattern Recognition.

[10]  Jon Atli Benediktsson,et al.  Intrinsic Image Decomposition for Feature Extraction of Hyperspectral Images , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[11]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[12]  Shutao Li,et al.  Image Fusion With Guided Filtering , 2013, IEEE Transactions on Image Processing.

[13]  Haoxiang Wang,et al.  An approach for hyperspectral image classification by optimizing SVM using self organizing map , 2017, J. Comput. Sci..

[14]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[15]  Li Xu,et al.  Structure extraction from texture via relative total variation , 2012, ACM Trans. Graph..

[16]  Saurabh Prasad,et al.  Limitations of Principal Components Analysis for Hyperspectral Target Recognition , 2008, IEEE Geoscience and Remote Sensing Letters.

[17]  Rob Heylen,et al.  Band-Specific Shearlet-Based Hyperspectral Image Noise Reduction , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Bo Du,et al.  Hyperspectral Target Detection via Adaptive Information - Theoretic Metric Learning with Local Constraints , 2018, Remote. Sens..

[19]  Haibo Wang,et al.  Large margin distribution machine for hyperspectral image classification , 2016, J. Electronic Imaging.

[20]  Guy Gilboa,et al.  Spectral Total-Variation Local Scale Signatures for Image Manipulation and Fusion , 2019, IEEE Transactions on Image Processing.

[21]  Bo Liu,et al.  Adaptive scalable kernel for hyperspectral image classification , 2019, J. Electronic Imaging.

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

[23]  Qingshan Liu,et al.  Patch-based active learning (PTAL) for spectral-spatial classification on hyperspectral data , 2014 .

[24]  Shutao Li,et al.  PCA-Based Edge-Preserving Features for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Jon Atli Benediktsson,et al.  Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Jun Li,et al.  Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Antonio J. Plaza,et al.  Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Haibo Wang,et al.  Albedo recovery for hyperspectral image classification , 2017, J. Electronic Imaging.

[29]  Jon Atli Benediktsson,et al.  Extended Random Walker-Based Classification of Hyperspectral Images , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Hamid Tairi,et al.  Motion detection using color structure-texture image decomposition , 2015, 2015 Intelligent Systems and Computer Vision (ISCV).

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

[32]  J. A. Gualtieri,et al.  Support vector machines for classification of hyperspectral data , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[33]  Qian Du,et al.  A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[34]  N. Lam,et al.  Wavelets for Urban Spatial Feature Discrimination: Comparisons with Fractal, Spatial Autocorrelation, and Spatial Co-Occurrence Approaches , 2004 .

[35]  Antonio J. Plaza,et al.  Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[36]  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.

[37]  Jon Atli Benediktsson,et al.  Hyperspectral Image Classification With Independent Component Discriminant Analysis , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Jon Atli Benediktsson,et al.  Hyperspectral Image Classification via Multiple-Feature-Based Adaptive Sparse Representation , 2017, IEEE Transactions on Instrumentation and Measurement.

[39]  James E. Fowler,et al.  Locality-Preserving Discriminant Analysis in Kernel-Induced Feature Spaces for Hyperspectral Image Classification , 2011, IEEE Geoscience and Remote Sensing Letters.

[40]  Jon Atli Benediktsson,et al.  Segmentation and classification of hyperspectral images using watershed transformation , 2010, Pattern Recognit..

[41]  Jonathan Cheung-Wai Chan,et al.  Hyperspectral Image Super-Resolution Based on Spatial and Spectral Correlation Fusion , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[42]  Jonathan Cheung-Wai Chan,et al.  Learning and Transferring Deep Joint Spectral–Spatial Features for Hyperspectral Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[43]  Fang Liu,et al.  Hyperspectral Image Classification by Spatial–Spectral Derivative-Aided Kernel Joint Sparse Representation , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.