Supervised classification of hyperspectral images using a combination of spectral and spatial information

This paper describes a new method for classification of hyperspectral images for extracting carthographic objects. The developed method is intended as a tool for automatic map updating. The idea is to use an existing map of the region of interest as a learning set. The proposed method is based on logistic regression. Logistic regression (LR) is a supervised multi-variate statistical tool that finds an optimal combination of the input channels for distinguishing one class from all the others. LR thus results in detection images per class. These can be combined into a classification image. The LR method that is used here is a step-wise optimisation that also performs a channel selection. The results of the LR are further improved by taking into account spatial information by means of a region growing method. The parameters of the region growing are optimised for each class of interest. For each class the optimal set of parameters is determined. The method is applied on a HyMap hyperspectral image of an area in Southern Germany and the results are compared to those of classical methods. For the comparison a ground truth image was created by combining data from a cadaster map and a digital topographic map.