Two-Stage Clustering Technique Based on the Neighboring Union Histogram for Hyperspectral Remote Sensing Images

This paper presents a novel unsupervised classification approach suited to hyperspectral remote sensing image data sets that uses <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-means clustering combined with the neighboring union histogram (NUH). The approach is implemented in five main steps as follows: 1) dividing the hyperspectral image data intt uncorrelated groups based on the correlation coefficient matrix; 2) extracting the first few principal component analysis (PCA) components from each group; 3) computing the NUHs of every group; 4) obtaining several relatively rough clustering results by employing the first-stage <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-means procedure to classify every group’s NUHs; and 5) using the second-stage <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-means procedure to refine the rough clustering results for the final clustering map. The NUH indicates the regional statistical features of a point, thereby making the proposed approach insensitive to noise and abnormal data. The two-stage clustering technique improves the recognition rate of similar land cover classes. The proposed approach is compared with five other unsupervised methods. The experimental results on two different types of real-world hyperspectral remote sensing images validate the high accuracy of the proposed approach.

[1]  Jun Li,et al.  Improved hyperspectral image classification by active learning using pre-designed mixed pixels , 2016, Pattern Recognit..

[2]  Liangpei Zhang,et al.  Unsupervised remote sensing image classification using an artificial immune network , 2011 .

[3]  Nojun Kwak,et al.  Principal Component Analysis Based on L1-Norm Maximization , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Geert M. P. van Kempen,et al.  Segmentation of multi-spectral images using the combined classifier approach , 2003, Image Vis. Comput..

[5]  Lei Zhang,et al.  Band-Subset-Based Clustering and Fusion for Hyperspectral Imagery Classification , 2011, IEEE Transactions on Geoscience and Remote Sensing.

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

[7]  Dinggang Shen,et al.  Image registration by local histogram matching , 2007, Pattern Recognit..

[8]  R. Brown,et al.  Separating near surface thermal inertia signals from a thermal time series by standardized principal component analysis , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[9]  Peijun Du,et al.  Hyperspectral Remote Sensing Image Classification Based on Rotation Forest , 2014, IEEE Geoscience and Remote Sensing Letters.

[10]  David A. Landgrebe,et al.  Hyperspectral image data analysis , 2002, IEEE Signal Process. Mag..

[11]  Paolo Gamba,et al.  Hierarchical Hybrid Decision Tree Fusion of Multiple Hyperspectral Data Processing Chains , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Richard Lucas,et al.  Hyperspectral remote sensing of peatland floristic gradients , 2015 .

[13]  Federico Tombari,et al.  SHOT: Unique signatures of histograms for surface and texture description , 2014, Comput. Vis. Image Underst..

[14]  Lorenzo Bruzzone,et al.  Kernel-based methods for hyperspectral image classification , 2005, IEEE Transactions on Geoscience and Remote Sensing.

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

[16]  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.

[17]  Le Wang,et al.  Sub-pixel confusion-uncertainty matrix for assessing soft classifications , 2008 .

[18]  Jing Wang,et al.  Visual data denoising with a unified Schatten-p norm and ℓq norm regularized principal component pursuit , 2015, Pattern Recognit..

[19]  Fuchun Sun,et al.  A Fast and Robust Sparse Approach for Hyperspectral Data Classification Using a Few Labeled Samples , 2012, 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 Hyperspectral Image Segmentation Using S , 2022 .

[21]  Lorenzo Bruzzone,et al.  A Fuzzy-Statistics-Based Affinity Propagation Technique for Clustering in Multispectral Images , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Peter F. Fisher,et al.  Spatial analysis of remote sensing image classification accuracy , 2012 .

[23]  Salah Bourennane,et al.  Dimensionality Reduction Based on Tensor Modeling for Classification Methods , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[24]  H. Ghassemian,et al.  Feature space discriminant analysis for hyperspectral data feature reduction , 2015 .

[25]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[26]  V. Mani,et al.  Crop Stage Classification of Hyperspectral Data Using Unsupervised Techniques , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  M. Canty.  Image Analysis, Classification, and Change Detection in Remote Sensing , 2006 .

[28]  Caroline Fossati,et al.  Improvement of Classification for Hyperspectral Images Based on Tensor Modeling , 2010, IEEE Geoscience and Remote Sensing Letters.

[29]  Hugh G. Lewis,et al.  A generalized confusion matrix for assessing area estimates from remotely sensed data , 2001 .

[30]  Giles M. Foody,et al.  Local characterization of thematic classification accuracy through spatially constrained confusion matrices , 2005 .

[31]  Sankaran Mahadevan,et al.  An improved method to construct basic probability assignment based on the confusion matrix for classification problem , 2016, Inf. Sci..

[32]  Fang Liu,et al.  Coupled compressed sensing inspired sparse spatial-spectral LSSVM for hyperspectral image classification , 2015, Knowl. Based Syst..

[33]  Bor-Chen Kuo,et al.  A New Weighted Fuzzy C-Means Clustering Algorithm for Remotely Sensed Image Classification , 2011, IEEE Journal of Selected Topics in Signal Processing.

[34]  Melba M. Crawford,et al.  Active Learning via Multi-View and Local Proximity Co-Regularization for Hyperspectral Image Classification , 2011, IEEE Journal of Selected Topics in Signal Processing.

[35]  Congshan Zhu,et al.  Mean shift‐based clustering analysis of multispectral remote sensing imagery , 2009 .

[36]  D. Wang,et al.  Image and Texture Segmentation Using Local Spectral Histograms , 2006, IEEE Transactions on Image Processing.

[37]  Liangpei Zhang,et al.  An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery , 2006, IEEE Trans. Geosci. Remote. Sens..

[38]  Fulin Luo,et al.  Dimensionality reduction of hyperspectral images based on sparse discriminant manifold embedding , 2015 .

[39]  Isak Gath,et al.  Unsupervised Optimal Fuzzy Clustering , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  Witold Pedrycz,et al.  Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection , 2015, Comput. Geosci..

[41]  Luis Samaniego,et al.  Supervised Classification of Remotely Sensed Imagery Using a Modified $k$-NN Technique , 2008, IEEE Transactions on Geoscience and Remote Sensing.

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

[43]  Jun Li,et al.  A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination , 2015 .

[44]  Jon Atli Benediktsson,et al.  Ensemble Classification Algorithm for Hyperspectral Remote Sensing Data , 2009, IEEE Geoscience and Remote Sensing Letters.

[45]  R. Redner,et al.  Mixture densities, maximum likelihood, and the EM algorithm , 1984 .