Ranga Raju Vatsavai, Surya S. Durbha
Supervised learning (classification) is the most widely used technique for thematic classifification of remote sensing images. Statistical pattern recognition algorithms, especially the maximum likelihood classififier is most extensively studied and utilized for classification of multi-spectral images. Decision trees and neural networks have also been widely applied for multisource (remote sensing images and ancillary geospatial databases) classification. However, recent advances in remote sensing technology, especially improved spectral, spatial, and temporal resolutions, puts several constraints on the traditional classification algorithms. For example, increasing spectral and temporal resolution requires large number of training samples (typically 10 to 100 times the number of bands), and increasing spatial resolution invalidates the fundamental assumption that the training samples are independent and identically distributed (iid). These advancements in remote sensing data have prompted the development of advanced classification algorithms to overcome some of the challenges posed by new datasets. Unfortunately these new algorithms have been connected mostly to the academic researchers, and the current commercial implementations are oblivious to these advances. This tutorial tries to fill this gap by bringing the recent advances to the practitioners.
Dr. Ranga Raju Vatsavai is a research scientist in the Computational Sciences and Engineering Division at the Oak Ridge National Laboratory since December 2006. He received his masters and doctoral degree in computer science from the University of Minnesota. He has been conducting research in the area of remote sensing, spatial databases, and data mining for the past 20 years. Before joining ORNL, he worked at IBM-Research (2004-06; IIT-Delhi campus), Remote Sensing Lab at the U of Minnesota (1999-2004; Twin-cities, MN), AT&T Labs (1998; Middletown, NJ), Center for Development of Advanced Computing (1995-98; C-DAC, Pune, India), and National Forest Data Management Center (1990-95; FRI Campus, Dehradun, India). He has published over fifty peer-reviewed articles and served on program committees of several international conferences including ACM SIGKDD and SDM. He co-organized several workshops with leading conferences (Sensor-KDD and SSTDM) and co-edited two books. He also contributed to several highly successful software systems in various capacities (UMN-MapServer a world leading open source WebGIS, *Miner a spatiotemporal data mining workbench, EASI/PACE Parallel Fly!, Parallel SAR, and the first parallel softcopy photogrammetry system for IRS-1C/1D satellites). His research interests include remote sensing, data mining, machine learning, parallel computing, and computational geoinformatics.
Dr. Surya S. Durbha is an Assistant Research Professor at the Center for Advanced Vehicular Systems (CAVS) and also holds an adjunct faculty position with the electrical and computer engineering department at Mississippi State University. He received his M.S. degree in remote sensing from Andhra University, India, in 1997 and Ph.D degree in computer engineering from Mississippi State University (MSU), MS, U.S.A, in 2006. Earlier he was an application scientist at the Indian Institute of Re-mote Sensing, Department of Space, India (Dehradun, 1998 to 2001) and also worked in Rolta India limited (Mumbai, 1997). He is currently working in the area of image information mining tools for content-based knowledge retrieval from remote sensing imagery and in the recent past worked on the retrieval of biophysical variables from multi-angle satellite data. He is also working in the development of knowledge-based systems through an ongoing funding from NOAA-NGI on heterogeneous coastal sensor data sets integration through an information semantic-based framework and sensor web development. He has published over 25 peer reviewed articles, served on program committees of several international conferences including SSKI, SSTDM, and IGARSS, and co-chaired sessions at various conferences. His current research interests are semantics, knowledge-based systems, image information mining, remote sensing, and sensor webs.
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