Title :
Assessment of Multi-Sensor Neural Image Fusion and Fused Data Mining for Land Cover Classification
Author :
Pugh, M. ; Waxman, A. ; Fay, D.
Author_Institution :
Air Force Res. Lab., Rome, NY
Abstract :
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 multi-temporal imagery. For this investigation multi-season Landsat and Radarsat imagery of a forest region in central New York State was processed using opponent-band 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
Keywords :
data mining; fuzzy neural nets; image classification; image fusion; image texture; neural nets; radar imaging; satellite communication; New York State; Radarsat imagery; biologically-based image fusion; contour enhancement; earth observation satellite; forest region; fused data mining method; fuzzy ARTMAP neural classifier; multiscale visual texture; multiseason Landsat imagery; multisensor neural image fusion; opponent-band image fusion; Data mining; Electromagnetic spectrum; Feedforward neural networks; Image fusion; Laboratories; Neural networks; Remote sensing; Satellites; Sensor phenomena and characterization; Statistical analysis; Image fusion; land cover classification; neural network; pattern learning; pattern recognition;
Conference_Titel :
Information Fusion, 2006 9th International Conference on
Conference_Location :
Florence
Print_ISBN :
1-4244-0953-5
Electronic_ISBN :
0-9721844-6-5
DOI :
10.1109/ICIF.2006.301782
