We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Subpixel Urban Land Cover Estimation: Comparing Cubist, Random Forests, and Support Vector Regression.
- Authors
Walton, Jeffrey T.
- Abstract
Three machine learning subpixel estimation methods (Cubist, Random Forests, and support vector regression) were applied to estimate urban cover. Urban forest canopy cover and impervious surface cover were estimated from Landsat-7 ETM+ imagery using a higher resolution cover map resampled to 30 m as training and reference data. Three different band combinations (reflectance, tasseled cap, and both reflectance and tasseled cap plus thermal) were compared for their effectiveness with each of the methods. Thirty different training site number and size combinations were also tested. Support vector regression on the tasseled cap bands was found to be the best estimator for urban forest canopy cover, while Cubist performed best using the reflectance plus tasseled cap band combination when predicting impervious surface cover. More training data partitioned in many small training sites generally produces better estimation results.
- Subjects
URBAN land use; FORESTS &; forestry; MACHINE learning; ESTIMATION theory; FOREST canopies; REGRESSION analysis
- Publication
Photogrammetric Engineering & Remote Sensing, 2008, Vol 74, Issue 10, p1213
- ISSN
0099-1112
- Publication type
Article
- DOI
10.14358/PERS.74.10.1213