Remote Sensing Data Normalization

Abstract

The increasing access to remote sensing data from different platforms, acquired at different spatial, spectral, and temporal resolutions, is continuously widening the scope of applications of these datasets. Parallel advancements in computational science and technology have led to the development of more sophisticated data processing and analysis tools. While early remote sensing studies focused on detecting a feature or phenomenon, the current practice is to conduct multitemporal studies and time series analyses based on multiple data sources, including optical, microwave, and thermal imagery. There are several calibration and normalization issues that need to be resolved before these more complex monitoring and change detection studies can be accomplished.