His current emphasis involves developing automated interpretation algorithms to extract relevant information from remotely sensed imagery. Remote sensing images are generally quite large and require considerable time and effort to manually process and interpret. The recent higher resolution aerial and satellite-based sensors have compounded this problem. Automated feature extraction and classification schemes are preferable. Another current research theme is the application of such algorithms to the computer-aided interpretation of biomedical images.
Efficient algorithms ensure that the product is produced in a timely fashion.
For example, the design of improved dedicated unsupervised clustering approaches to quantify natural groupings of data is important. Accuracy is important to characterize the image data in a manner similar to the human operator. Texture feature extraction plays an important role in accurate interpretation of remotely sensed imagery. Care must be taken to account for the image formation model in the feature extraction process. Edited by C.T. Leondes, CRC Press, Chapter 2, 2001.