Classification of hyperspectral image using multiscale spatial texture features

Spatial information has shown significant contribution for hyperspectral image classification. Local Binary Pattern (LBP) can be used for extracting spatial texture features, however it is incapable of capturing textural and structural features of images at various resolution. Hence, we present a multiscale scheme on Complete LBP (CLBP) as well as on LBP to obtain better spatial features from hyperspectral imagery (HSI). Experiments conducted on two standard HSI datasets proved that the proposed multiscale scheme can improve the classification accuracy of both LBP and CLBP, and Multiscale CLBP provides better accuracy compared to the state-of-the-art spatial feature extraction methods for HSI classification.

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

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

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

[4]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Jon Atli Benediktsson,et al.  Adaptive Markov Random Fields for Joint Unmixing and Segmentation of Hyperspectral Images , 2013, IEEE Transactions on Image Processing.

[6]  Chen Chen,et al.  Reconstruction of Hyperspectral Imagery From Random Projections Using Multihypothesis Prediction , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[7]  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.

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

[9]  Chen Chen,et al.  Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine , 2014, Remote. Sens..

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

[11]  J. Shan,et al.  Principal Component Analysis for Hyperspectral Image Classification , 2002 .

[12]  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 .

[13]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Glenn Healey,et al.  Hyperspectral Region Classification Using a Three-Dimensional Gabor Filterbank , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Ping Tang,et al.  Spectral and spatial classification of hyperspectral data using SVMs and Gabor textures , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

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

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

[18]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.