A novel active learning method for support vector regression to estimate biophysical parameters from remotely sensed images

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 8537, Issue , 2012, Pages

  Demir, Begüm ^a   Bruzzone, Lorenzo ^a  

^a UNIVERSITY OF TRENTO   (Italy)

Author keywords

Active learning; Biophysical parameters estimation; Kernel k means clustering; Support vector regression

Indexed keywords

ACTIVE LEARNING; ACTIVE LEARNING METHODS; BIOPHYSICAL PARAMETERS; HIGH DENSITY REGIONS; KERNEL K-MEANS; NUMBER OF SAMPLES; REMOTELY SENSED IMAGES; SUPPORT VECTOR REGRESSION (SVR);

BIOPHYSICS; ESTIMATION; FORESTRY; REGRESSION ANALYSIS; REMOTE SENSING; SIGNAL PROCESSING; SPACE OPTICS; TUBES (COMPONENTS); UNCERTAINTY ANALYSIS;

PARAMETER ESTIMATION;

ESTIMATION; EXPERIMENTATION; PARAMETERS; REGRESSION ANALYSIS; REMOTE SENSING; SAMPLING; SIGNALS; TUBES;

EID: 84875684423     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.979666     Document Type: Conference Paper

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