Improving urban classification through fuzzy supervised classification and spectral mixture analysis

Authors:
J. Tang;L. Wang;S. W. Myint
Affiliations:
Department of Geography and Environmental Systems, University of Maryland, Baltimore County, Baltimore, USA;NCGIA, Department of Geography, University of Buffalo, State University of New York, Buffalo, USA;Department of Geography, Arizona State University, Tempe, USA
Venue:
International Journal of Remote Sensing
Year:
2007

Citing 2
Cited 1

Incorporating mixed pixels in the training, allocation and testing stages of supervised classifications

Pattern Recognition Letters - Special issue on non-conventional pattern analysis in remote sensing
Remote Sensing Digital Image Analysis: An Introduction

Remote Sensing Digital Image Analysis: An Introduction

Wavelet-fuzzy hybridization: Feature-extraction and land-cover classification of remote sensing images

Applied Soft Computing

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Abstract

In this study, a fuzzy-spectral mixture analysis (fuzzy-SMA) model was developed to achieve land use/land cover fractions in urban areas with a moderate resolution remote sensing image. Differed from traditional fuzzy classification methods, in our fuzzy-SMA model, two compulsory statistical measurements (i.e. fuzzy mean and fuzzy covariance) were derived from training samples through spectral mixture analysis (SMA), and then subsequently applied in the fuzzy supervised classification. Classification performances were evaluated between the 'estimated' landscape class fractions from our method and the 'actual' fractions generated from IKONOS data through manual interpretation with heads-up digitizing option. Among all the sub-pixel classification methods, fuzzy-SMA performed the best with the smallest total_MAE (MAE, mean absolute error) (0.18) and the largest Kappa (77.33%). The classification results indicate that a combination of SMA and fuzzy logic theory is capable of identifying urban landscapes at sub-pixel level.