Comparison of backpropagation, cascade-correlation and Kokonen algorithms for cloud retrieval

The expected high volume imagery data from Nasa Mission to the Planet Earth is one of the target application areas for automated cloud retrieval, and more generally for automated image classification. We used the backpropagation (BP), the cascade-correlation (CC) and Kohonen self-organizing map (SOM) neural network architectures for cloud retrieval from satellite imagery. We have used a simple scene (a mixed scene containing only cloud and ocean). This simple scene allows us to evaluate the accuracy of the classification better than a complicated scene. Both BP and CC performed at the same accuracy level, while the SOM algorithm was slightly less accurate in performing unsupervised learning. This study shows that for simple scenes, which are abundant in global monitoring satellite imagery, a simple pixel-by-pixel or 3x3 window approaches provide high accuracy classification without using complicated contextual information.