Integrating spectral and textural features for urban land cover classification with hyperspectral data

This paper presents a supervised classification framework that integrates discrete wavelet transform (DWT) based spectral and textural features for the urban land cover classification using hyperspectral data. Investigations involved application of 1-D DWT along the wavelength dimension of the hyperspectral data followed by 2-D DWT along spatial dimensions for spectral and texture feature extraction respectively. The combined spectral textural feature set is used for classification. The pixel wise classification on ROSIS data using SVM reveals that integration of spectral and textural information can better characterize the urban areas and statistically significantly improves the classification accuracy.

[1]  David A. Landgrebe,et al.  Feature Extraction Based on Decision Boundaries , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Qian Du,et al.  Interference and noise-adjusted principal components analysis , 1999, IEEE Trans. Geosci. Remote. Sens..

[3]  Alexander F. H. Goetz,et al.  Three decades of hyperspectral remote sensing of the Earth: a personal view. , 2009 .

[4]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[5]  David A. Landgrebe,et al.  Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data , 1998, IEEE Trans. Syst. Man Cybern. Part C.

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

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

[8]  Jiang Li,et al.  Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction , 2002, IEEE Trans. Geosci. Remote. Sens..

[9]  Filiberto Pla,et al.  Spectral–Spatial Pixel Characterization Using Gabor Filters for Hyperspectral Image Classification , 2013, IEEE Geoscience and Remote Sensing Letters.

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[11]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[12]  Cedric Nishan Canagarajah,et al.  Dimensionality Reduction of Hyperspectral Images Using Empirical Mode Decompositions and Wavelets , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Fuan Tsai,et al.  Feature Extraction of Hyperspectral Image Cubes Using Three-Dimensional Gray-Level Cooccurrence , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[14]  O. Dikshit,et al.  Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city, Uttar Pradesh , 2001 .