Efficient statistical classification of satellite measurements

Abstract

Supervized statistical classification is a vital tool for satellite image processing. It is useful not only when a discrete result, such as feature extraction or surface type, is required, but also for continuum retrievals by dividing the quantity of interest into discrete ranges. Because of the high resolution of modern satellite instruments and because of the requirement for real-time processing, any algorithm has to be fast to be useful. Here we describe an algorithm based on kernel estimation called Adaptive Gaussian Filtering that incorporates several innovations to produce superior efficiency as compared to three other popular methods: k-nearest-neighbour (KNN), Learning Vector Quantisation (LVQ) and Support Vector Machines (SVM). This efficiency is gained with no compromises: accuracy is maintained, while estimates of the conditional probabilities are returned. These are useful not only to gauge the accuracy of an estimate in the absence of its true value, but also to re-calibrate a retrieved image and as a proxy for a discretized continuum variable. The algorithm is demonstrated and compared with the other three on a pair of synthetic test classes and to map the waterways of the Netherlands.

Acknowledgements

Thank you very much to my colleagues from the IUP, University of Bremen for their continued support and encouragement of this work, in particular Stefan Buehler, Georg Heygster and Oliver Lemke. Thanks to Christian Melsheimer for his very valued comments on the draft manuscript.

This work was partially funded by the German Federal Ministry of Education and Research (BMBF), within the AFO2000 project UTH-MOS, grant 07ATC04. It is a contribution to COST Action 723 ‘Data Exploitation and Modeling for the Upper Troposphere and Lower Stratosphere’.