A spatial–spectral kernel-based approach for the classification of remote-sensing images

Classification of remotely sensed images with very high spatial resolution is investigated. The proposed method deals with the joint use of the spatial and the spectral information provided by the remote-sensing images. A definition of an adaptive neighborhood system is considered. Based on morphological area filtering, the spatial information associated with each pixel is modeled as the set of connected pixels with an identical gray value (flat zone) to which the pixel belongs: The pixel's neighborhood is characterized by the vector median value of the corresponding flat zone. The spectral information is the original pixel's value, be it a scalar or a vector value. Using kernel methods, the spatial and spectral information are jointly used for the classification through a support vector machine formulation. Experiments on hyperspectral and panchromatic images are presented and show a significant increase in classification accuracies for peri-urban area: For instance, with the first data set, the overall accuracy is increased from 80% with a conventional support vectors machines classifier to 86% with the proposed approach. Comparisons with other contextual methods show that the method is competitive.

► Adaptive neighborhood definition for the analysis of hyperspectral remote-sensing image.
► Classification with SVM using the composition of kernels.
► Classification accuracies are improved using such kernels.