Robert Kremens

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We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of wildfire behavior from real-time weather data, images, and sensor streams. The system should change the forecast when new data is received. The basic approach is to encapsulate the model code and use an ensemble Kalman filter in time-space.(More)
A proposed system for real-time modeling of wildfires is described. The system involves numerical weather and fire prediction, automated data acquisition from Internet sources, and input from aerial photographs and sensors. The system will be controlled by a non-Gaussian ensemble filter capable of assimilating out-of-order data. The computational model will(More)
—We propose a hybrid contextual fire detection algorithm for airborne and satellite thermal images. The proposed algorithm essentially treats fire pixels as anomalies in images and can be considered a special case of the more general clutter or background suppression problem. It utilizes the local background around a potential fire pixel and discriminates(More)
—We present a method to improve subpixel signal detection in airborne or orbital image sequences. The proposed technique recognizes stable interest point features in multiple overlapping frames. It estimates motion between consecutive frames and tracks candidate detections over time. The final detection decision combines signal strengths from multiple views(More)
This study investigates small aerosol particles as a source of an imaging phenomenon observed in thermal remote sensing data. The phenomenon is characterized by degraded atmospheric transmissions in the thermal infrared while high transmissions (clear conditions) are observed in the visible wavelength region. This atmospheric anomaly has been linked to(More)
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