&lt;title&gt;Neural processing of SAR imagery for enhanced target detection&lt;/title&gt;

Waxman, Allen M.; Lazott, Carol H.; Fay, David A.; Gove, Alan N.; Steele, William

doi:10.1117/12.210839

Cited by 3 publications

(1 citation statement)

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“…Based on models of spatial-opponent processing in the bipolar and horizontal cells of the human retina, modified center-surround shunting neural networks [ 11 ] were used to adaptively normalize and contrast enhance each of the co-registered panchromatic, visible, infrared, thermal, and SAR images prior to multi-band and multi-sensor fusion. This process provides a high frequency boost in combination with ratio processing on three scales for each pixel in the image.…”

Section: Image Conditioningmentioning

confidence: 99%

Assessment of Multi-Sensor Neural Image Fusion and Fused Data Mining for Land Cover Classification

Pugh

Waxman

Fay

2006

2006 9th International Conference on Information Fusion

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Recent studies suggest that the combination of imagery from earth observation satellites with complementary spectral, spatial, and temporal information may provide improved land cover classification performance. This paper assesses the benefits of new biologically-based image fusion and fused data mining methods for improving discrimination between spectrally-similar land cover classes using multi-spectral, multi-sensor, and multitemporal imagery. For this investigation multi-season Landsat and Radarsat imagery of a forest region in central New York State was processed using opponentband image fusion, multi-scale visual texture and contour enhancement, and the Fuzzy ARTMAP neural classifier. These methods are shown to enable identification of sub-categories of land cover and provide improved classification accuracy compared to traditional statistical methods.

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Section: Image Conditioningmentioning

confidence: 99%