Neural image fusion of remotely sensed electro-optical and synthetic aperture radar data for forest classification

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn 1, Issue , 2004, Pages 617-620

  Pugh, Mark L ^a   Duggin, Michael J ^b   Waxman, Allen M ^c   Hassett, James M ^d  

^a AIR FORCE RESEARCH LABORATORY   (United States)

^b AIR FORCE RESEARCH LABORATORY   (United States)

^c BAE SYSTEMS   (United Kingdom)

^d STATE UNIVERSITY OF NEW YORK   (United States)

Author keywords

Forest classification; Fusion; Neural networks

Indexed keywords

EARTH OBSERVATION SATELLITES; FOREST CLASSIFICATION; IMAGE FUSION; MULTISENSOR IMAGERY;

DATA ACQUISITION; DATA MINING; ELECTROOPTICAL DEVICES; ENVIRONMENTAL ENGINEERING; FORESTRY; FUSION REACTIONS; NEURAL NETWORKS; OPTICAL RESOLVING POWER; SATELLITE OBSERVATORIES; SENSOR DATA FUSION; SYNTHETIC APERTURE RADAR;

REMOTE SENSING;

EID: 15944425407     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. th International Military Sensing Symposium, Gathersburg, MD, 2002
2. L. Warnecke, R. V. Nanni, Z. Nedovic-Budic, W. Stiteler IV, "Remote sensing in the nation's 50 state forestry organizations", GeoManagent Associates Inc., Syracuse, NY, 2002
3. D. G. Leckie, "Synergism of synthetic aperture radar and visible/infrared data for forest type discriminination", Photogrammetric Engineering and Remote Sensing", 56:1237-1246
4. F. M. Henderson, R. Chasan, J. Portolese, and T. Hart, Jr., "Evaluation of SAR-optical imagery synthesis techniques in a complex coastal ecosystem", Photogrammetric Engineering and Remote Sensing", 68:839-846
5. User's Guide - "Neural fusion: multisensor image fusion and mining module for ERDAS Imagine" Alphatech, Inc., Burlington, MA. 2004