Hyperspectral image classification using multiple weighted local kernel matrix descriptors

ABSTRACT

Local covariance matrix descriptor is a new spatial-spectral feature generation method. It has been successfully applied for remote sensing image classification. Meanwhile, there are some critiques of it because it neglects nonlinear relationships between features, which are serious when applied to hyperspectral images (HSIs). So, the present paper aims to develop weighted local kernel matrix (WLKM) descriptors for the spatial-spectral classification of HSI. The developed weighted local kernel matrix features, including spectral-textural-geometrical aspects, have been used in two classification schemes proposed. In the first approach, called ‘early fusion’, the weighted sum of WLKM descriptors derived from spectral and spatial features is classified using the log-Euclidean kernel SVM. In the second approach, called ‘late fusion’, a multiple log-Euclidean kernel SVM strategy based on the WLKM descriptors of spatial and spectral features is developed for HSI classification. Experiments on three widely used HSI datasets have proved the superiority of the proposed approaches over some recent spatial-spectral HSI classification techniques.

KEYWORDS:

• Hyperspectral image
• Kernel matrix
• Gabor filters
• Log-Euclidean kernel SVM
• Spatial-spectral features
• Multiple kernel learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Hyperspectral data

Hyperspectral data are available at http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes