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16 July 1999 Removing ocean surface clutter in multispectral and hyperspectral imagery
Dennis M. Silva, Ikram E. Abdou
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Proceedings Volume 3717, Algorithms for Multispectral and Hyperspectral Imagery V; (1999) https://doi.org/10.1117/12.353040
Event: AeroSense '99, 1999, Orlando, FL, United States
Abstract
We present two approaches for removing ocean surface (and subsurface) clutter in multispectral and hyperspectral imagery. The first approach is a computationally simple, physics-based algorithm that models surface clutter as a two-component mixture of surface-reflected light (glint) and scattered upwelling light. The model exploits the difference in the spectral content of the two components in order to differentiate glint from subsurface scattered light. The second approach is a statistics-based algorithm that simultaneously models the local spectral and spatial correlation structure with a linear predictive filter that spatially adapts to the statistical properties of the image on a region-by-region basis as determined by a spectrally generated segmentation map. The filter coefficients for each image segment are estimated via autoregression using a multichannel, multidimensional formulation of the Yule- Walker equations. The combined spatial-spectral processing allows the filter predictor to remove subsurface background clutter, as well as glint. In both algorithms, decluttered residuals are obtained by subtracting the background estimate from the input image data. In a test of effectiveness, the data conditioning provided by decluttering on a regional basis improved the performance of our set of matched-filter detectors by an average of 3 dB. The performance enhancement demonstrates the need for regional estimates of background when the assumption of spatially stationary data is no longer valid. Examples of both decluttering and matched-filter detection processing are presented using data collected by the AAHIS sensor.
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Dennis M. Silva and Ikram E. Abdou "Removing ocean surface clutter in multispectral and hyperspectral imagery", Proc. SPIE 3717, Algorithms for Multispectral and Hyperspectral Imagery V, (16 July 1999); https://doi.org/10.1117/12.353040
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KEYWORDS
Image segmentation
Sensors
Data modeling
Image processing algorithms and systems
Detection and tracking algorithms
Image processing
Optical filters
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