Hyperspectral image classification based on volumetric texture and dimensionality reduction

doi:10.1007/s11707-014-0473-4

Front. Earth Sci.

2015, Vol. 9

Issue (2) : 225-236 https://doi.org/10.1007/s11707-014-0473-4

RESEARCH ARTICLE

Hyperspectral image classification based on volumetric texture and dimensionality reduction

Hongjun SU^1,²,Yehua SHENG³,Peijun DU^2,^*(

),Chen CHEN⁴,Kui LIU⁴

1. School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
2. Key Laboratory of Satellite Mapping Technology and Application, National Administration of Surveying, Mapping and Geoinformation, Nanjing University, Nanjing 210046, China
3. Key Laboratory of Virtual Geographic Environment (Ministry of Education), Nanjing Normal University, Nanjing 210023, China
4. Department of Electrical Engineering, the University of Texas at Dallas, Richardson, TX 75080-3021, USA

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Abstract

A novel approach using volumetric texture and reduced-spectral features is presented for hyperspectral image classification. Using this approach, the volumetric textural features were extracted by volumetric gray-level co-occurrence matrices (VGLCM). The spectral features were extracted by minimum estimated abundance covariance (MEAC) and linear prediction (LP)-based band selection, and a semi-supervised k-means (SKM) clustering method with deleting the worst cluster (SKMd) band-clustering algorithms. Moreover, four feature combination schemes were designed for hyperspectral image classification by using spectral and textural features. It has been proven that the proposed method using VGLCM outperforms the gray-level co-occurrence matrices (GLCM) method, and the experimental results indicate that the combination of spectral information with volumetric textural features leads to an improved classification performance in hyperspectral imagery.

Keywords fusion hyperspectral imagery image classification volumetric textural feature spectral feature

Corresponding Author(s): Peijun DU

Online First Date: 14 November 2014 Issue Date: 30 April 2015

Cite this article:

Hongjun SU,Yehua SHENG,Peijun DU, et al. Hyperspectral image classification based on volumetric texture and dimensionality reduction[J]. Front. Earth Sci., 2015, 9(2): 225-236.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-014-0473-4
https://academic.hep.com.cn/fesci/EN/Y2015/V9/I2/225

Fig.1 Hyperspectral image cube for Washington DC Mall data

Fig.2 Texture computation of GLCM and VGLCM

Tab.1 Directions of pairwise pixels for VGLCM

Fig.3 Two-dimensional (2D; left) and three-dimensional (3D; right) semi-variogram analysis for texture extraction

Fig.4 Classification framework for hyperspectral image analysis

Tab.2 Feature fusion schemes for hyperspectral image classification

Tab.3 Training and test samples for DC Mall data

Tab.4 Training and test samples for Indian Pines

Fig.5 Texture samples for different objects

Fig.6 Various box sizes for VGLCM of D.C. Mall dataset. (a) Road I; (b) Road II; (c) Trees I; (d) Trees II; (e) Grass I; (f) Grass II; (g) Building I; (h) Building II

Fig.7 Various window sizes for GLCM of D.C. Mall dataset

Fig.8 Textures extracted using GLCM (left) and VGLCM (right). (a) Variance; (b) Contrast; (c) Dissimilarity; (d) Energy; (e) Entropy; (f) Homogeneity

Fig.9 Classification maps using GLCM and VGLCM textures for D.C. Mall data. (a) 5 selected bands+6 texture features (GLCM); (b) 5 selected bands+6 texture features (VGLCM); (c) 5 clusters+6 texture features (GLCM); (d) 5 clusters+6 texture features (VGLCM)

Tab.5

Tab.6 Classification results for spectral and texture feature fusion

Fig.10 Classification comparison using spectral and texture data

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