TY - JOUR
T1 - Active learning for detecting a spectrally variable subject in color infrared imagery
AU - Foschi, Patricia G.
AU - Liu, Huan
N1 - Funding Information:
This research has been supported by the California Department of Boating and Waterways. Deepak Kolippakkam and Amit Mandvikar at ASU have participated in developing systems and running experiments and have drawn figures and compiled tables. Gary Fields, Yukari Matsumoto, and Mami Odaya at SFSU have participated in ground data collection, image interpretation, and training/testing data selection.
PY - 2004/10/1
Y1 - 2004/10/1
N2 - To classify Egeria densa, Brazilian waterweed, in scan-digitized color infrared aerial photographs, we are developing an interactive computer system based on data-mining techniques with active learning capabilities. Key components of the system are: feature extraction, automatic classification, active learning, and experimental evaluation.
AB - To classify Egeria densa, Brazilian waterweed, in scan-digitized color infrared aerial photographs, we are developing an interactive computer system based on data-mining techniques with active learning capabilities. Key components of the system are: feature extraction, automatic classification, active learning, and experimental evaluation.
KW - Active learning
KW - CIR imagery
KW - Data mining
KW - Feature extraction
UR - http://www.scopus.com/inward/record.url?scp=4544332136&partnerID=8YFLogxK
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U2 - 10.1016/j.patrec.2004.06.004
DO - 10.1016/j.patrec.2004.06.004
M3 - Conference article
AN - SCOPUS:4544332136
SN - 0167-8655
VL - 25
SP - 1509
EP - 1517
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
IS - 13
T2 - Pattern Recognition for Remote Sensing
Y2 - 1 August 2002 through 1 August 2002
ER -