Skip to Main Content
Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regressionAuthor(s): Jeffrey T. Walton
Source: Photogrammetric Engineering & Remote Sensing. 74(10): 1213-1222.
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
PDF: Download Publication (506.3 KB)
DescriptionThree 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.
- Check the Northern Research Station web site to request a printed copy of this publication.
- Our on-line publications are scanned and captured using Adobe Acrobat.
- During the capture process some typographical errors may occur.
- Please contact Sharon Hobrla, firstname.lastname@example.org if you notice any errors which make this publication unusable.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationWalton, Jeffrey T. 2008. Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression. Photogrammetric Engineering & Remote Sensing. 74(10): 1213-1222.
- Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data.
- Application of two regression-based methods to estimate the effects harvest on forest structure using Landsat data
- Relationship between LiDAR-derived forest canopy height and Landsat images
XML: View XML