Crop discrimination using multi-temporal SAR imagery

Seven ERS-1 SAR images obtained at different dates during the 1993 crop growing season are used in a study of the potential of multi-temporal SAR for agricultural crop discrimination for an area near Feltwell, Norfolk, UK. The study compares a per-pixel and a per-field approach. Pixel-based classification is based on raw intensity images, temporal subtraction images, filtered images, and texture features. Field-based classification uses the mean back-scatter coefficient derived for each field. Analysis of the contribution of each dataset uses statistical separability measures and confusion matrix methods. The classification algorithms used are maximum likelihood and Kohonen's self-organized feature map (SOM). We find that SAR-based texture features contribute nothing to crop discrimination. Filtered images produce the best result for the per-pixel approach, giving a classification accuracy of around 60%. The use of a SOM for field-based classification produces a classification accuracy greater than 75%. This is not a surprising result, as field-based classifications use averaged data, in which the noise effect is reduced.