A Comparison of the Characterisation of Agricultural Land Using Singular Value Decomposition and Neural Networks

Summary

Methods are defined and tested for the characterisation of agricultural land from multi-spectral imagery, based on Singular Value Decomposition (SVD) and Artificial neural networks (ANN). The SVD technique, which bears a close resemblance to multivariate statistic techniques, has previously been successfully applied to problems of signal extraction for marine data [1] and forestry species classification [2].

In this study the two techniques are used as a classifier for agricultural regions, using airborne Daedalus ATM data, with lm resolution. The specific region chosen is an experimental research farm in Bavaria, Germany. This farm has a large number of crops, within a very small region and hence is not amenable to existing techniques. There are a number of other significant factors which render existing techniques such as the maximum likelihood algorithm less suitable for this area. These include a very dynamic terrain and tessellated pattern soil differences, which together cause large variations in the growth characteristics of the crops.

Both the SVD and ANN techniques are applied to this data set using a multistage classification approach. Typical classification accuracy’s for the techniques are of the order of 85–100%. Preliminary results indicate that the methods provide fast and efficient classifiers with the ability to differentiate between crop types such as Wheat, Rye, Potatoes and Clover.