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  1. Real-Time Change Detection for Wide Area Surveillance.

    Abstract not provided.
  2. Semi-Supervised Classification of Terrain Features in Polarimetric SAR Images.

    Abstract not provided.
  3. Semi-Supervised Classification of Terrain Features in Polarimetric SAR Images.

    Abstract not provided.
  4. Robust real-time change detection in high jitter.

    A new method is introduced for real-time detection of transient change in scenes observed by staring sensors that are subject to platform jitter, pixel defects, variable focus, and other real-world challenges. The approach uses flexible statistical models for the scene background and its variability, which are continually updated to track gradual drift in the sensor's performance and the scene under observation. Two separate models represent temporal and spatial variations in pixel intensity. For the temporal model, each new frame is projected into a low-dimensional subspace designed to capture the behavior of the frame data over a recent observation window. Per-pixelmore » temporal standard deviation estimates are based on projection residuals. The second approach employs a simple representation of jitter to generate pixelwise moment estimates from a single frame. These estimates rely on spatial characteristics of the scene, and are used gauge each pixel's susceptibility to jitter. The temporal model handles pixels that are naturally variable due to sensor noise or moving scene elements, along with jitter displacements comparable to those observed in the recent past. The spatial model captures jitter-induced changes that may not have been seen previously. Change is declared in pixels whose current values are inconsistent with both models.« less
  5. A new maximum-likelihood change estimator for two-pass SAR coherent change detection

    In previous research, two-pass repeat-geometry synthetic aperture radar (SAR) coherent change detection (CCD) predominantly utilized the sample degree of coherence as a measure of the temporal change occurring between two complex-valued image collects. Previous coherence-based CCD approaches tend to show temporal change when there is none in areas of the image that have a low clutter-to-noise power ratio. Instead of employing the sample coherence magnitude as a change metric, in this paper, we derive a new maximum-likelihood (ML) temporal change estimate—the complex reflectance change detection (CRCD) metric to be used for SAR coherent temporal change detection. The new CRCD estimatormore » is a surprisingly simple expression, easy to implement, and optimal in the ML sense. As a result, this new estimate produces improved results in the coherent pair collects that we have tested.« less
  6. A statistics-based approach to binary image registration with uncertainty analysis.

    Abstract not provided.

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"Simonson, Katherine Mary"

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