Skip to Main Content
Often, previous knowledge about the classes on the ground may assist in the improvement of a land cover classification from remote sensing data. There are many ways in which the previous information in the form of a priori probabilities can be included in the training stage of a supervised classification process. The inclusion of a priori probabilities may be useful to classify the images dominated by mixed pixels. In this paper, an approach to incorporate a priori probabilities in back propagation neural network (BPNN) algorithm, by way of replicating the training data (consisting of pure pixels) of a class according to its proportional area coverage has been implemented. By doing this, the abundant classes are assigned more weights than the other classes in the image. The results show a significant improvement in classification accuracy by 20% over the case when the a priori probabilities are not included. However, the major limitation of this approach is that it depends on the use of pure pixels, which may often be hard to find in images dominated by mixed pixels. We therefore suggest an alternative approach that incorporates mixed pixels in the training stage of BPNN algorithm. The results from this approach also reflect a significant improvement in classification accuracy by 14%. Further, both the approaches produce significantly higher accuracy than the most widely used maximum likelihood classifier (MLC). However, the second approach, which does not depend on identification of pure pixels in the image and their replication, appears more attractive to produce meaningful and accurate land cover classification from remote sensing data.