Satellite image classification by integrating multiple AI methods

Collaborators: Dr. Giorgos Mountrakis, Lori Luo, Dr. R. Watts

Integrating multiple classifiers
Integrating multiple classifiers

Funders: Multiple sources.

Motivation: Remote sensing as a field of study has reached its adulthood; computer-assisted classifiers have been in development for more than two decades. The complexity of remote sensing classification has led to a variety of methods, some of them based on artificial intelligence (AI). Recently, we have also observed a significant increase in parallel processing capabilities. Computer workstations with multiple processors are becoming the mainstream in research laboratories. Physical limitations in processor design indicate that future computational power improvements will result from parallel processing rather than single processor advances. Parallel computing presents a unique opportunity and challenge for image classifiers. The question arises: how can we harvest this new power to improve classification results?

Methodology: One solution is to implement hybrid classifiers, i.e. methods that merge multiple approaches together. When multiple classifiers are integrated correctly, the ability to harvest the power of multiple algorithms with a critical eye on the application at hand improves classification accuracy.

Findings: We have shown that hybrid classifiers perform similarly or better than single classifiers. We are currently focusing on intelligent selection from multiple classifiers and adjustments in the multi-step process.

Relevant papers:
L. Luo, G. Mountrakis (to appear). Integrating intermediate inputs from partially classified images within a hybrid classification framework: An impervious surface estimation example. Remote Sensing of Environment.

G. Mountrakis, R. Watts, L. Luo, J. Wang (2009). Developing Collaborative Classifiers using an Expert-based Model. Photogrammetric Engineering and Remote Sensing, 75(7):831-844.[pdf]

G. Mountrakis (2008). Next generation classifiers: Focusing on integration frameworks. Highlight article for Photogrammetric Engineering and Remote Sensing, 74(10):1178-1180.[pdf]

More papers in preparation.