4.6 Article

Integrating multiple texture methods and NDVI to the Random Forest classification algorithm to detect tea and hazelnut plantation areas in northeast Turkey

Related references

Note: Only part of the references are listed.
Article Remote Sensing

Land cover classification using CHRIS/PROBA images and multi-temporal texture

Huiran Jin et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2012)

Article Remote Sensing

Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment

Robert Gilmore Pontius et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2011)

Article Environmental Sciences

Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery

Jennifer D. Watts et al.

REMOTE SENSING OF ENVIRONMENT (2011)

Article Environmental Sciences

A new approach for finding an appropriate combination of texture parameters for classification

Virendra Pathak et al.

GEOCARTO INTERNATIONAL (2010)

Article Computer Science, Interdisciplinary Applications

Empirical characterization of random forest variable importance measures

Kelfie J. Archer et al.

COMPUTATIONAL STATISTICS & DATA ANALYSIS (2008)

Article Ecology

Random forests for classification in ecology

D. Richard Cutler et al.

ECOLOGY (2007)

Article Computer Science, Artificial Intelligence

Random Forests for land cover classification

PO Gislason et al.

PATTERN RECOGNITION LETTERS (2006)

Article Remote Sensing

Random forest classifier for remote sensing classification

M Pal

INTERNATIONAL JOURNAL OF REMOTE SENSING (2005)

Article Geography, Physical

Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy

GM Foody

PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING (2004)

Article Remote Sensing

A comparison of texture measures for the per-field classification of Mediterranean land cover

CD Lloyd et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2004)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)