Dr. Kerekes has worked throughout his career on advancing the state of the art and practice of remote sensing technology through theoretical investigations, data analyses, and modeling of remote sensing systems. His interest has been in viewing the end-to-end remote sensing process as a system with application performance as the system metric. Developing models with this perspective has improved understanding of parameter sensitivities and requirements for system design and operation. His work has emphasized the use of statistical parametric models in propagating the information bearing characteristics of the scene through the effects of the remote sensing process. He has applied this approach to the study of multispectral remote sensing systems designed for surface land cover classification, the vertical profiling of atmospheric temperature and water vapor, and for unresolved (sub-pixel) object detection and identification. He has also investigated the use of spectral imaging for medical applications.

The field of remote sensing is multi-disciplinary by nature and Dr. Kerekes has developed, through professional interactions and study, a broad understanding and appreciation of the application side of remote sensing systems. This perspective has Dr. Kerekes well positioned to continue to contribute to collaborative research efforts that apply electrical engineering and remote sensing technologies to increasingly critical areas such as homeland security, site specific farm management, climate monitoring, resource monitoring, urban growth, and sustainable development. He also has interests in applying these concepts to the development and use of other multidimensional imaging systems such as those used for biomedical laboratory and industrial manufacturing applications.