SHORT INTRO

The study presents an image processing and analysis pipeline that combines object-based image
analysis with a Support Vector Machine to derive a multi-layered settlement product from Landsat-8
data over large areas. 43 image scenes are processed over large par ...

ALL DETAILS

Large-area settlement pattern recognition from Landsat-8 data. ISPRS Journal of Photogrammetry and Remote Sensing

Authors

Wieland M, Pittore M

Hazard

Topic

Year

2016

The study presents an image processing and analysis pipeline that combines object-based image
analysis with a Support Vector Machine to derive a multi-layered settlement product from Landsat-8
data over large areas. 43 image scenes are processed over large parts of Central Asia (Southern
Kazakhstan, Kyrgyzstan, Tajikistan and Eastern Uzbekistan). The main tasks tackled by this work
include built-up area identification, settlement type classification and urban structure types pattern
recognition. Besides commonly used accuracy assessments of the resulting map products, thorough
performance evaluations are carried out under varying conditions to tune algorithm parameters and
assess their applicability for the given tasks. As part of this, several research questions are being
addressed. In particular the influence of the improved spatial and spectral resolution of Landsat-8 on
the SVM performance to identify built-up areas and urban structure types are evaluated. Also the
influence of an extended feature space including digital elevation model features is tested for
mountainous regions. Moreover, the spatial distribution of classification uncertainties is analyzed and
compared to the heterogeneity of the building stock within the computational unit of the segments. The
study concludes that the information content of Landsat-8 images is sufficient for the tested
classification tasks and even detailed urban structures could be extracted with satisfying accuracy.
Freely available ancillary settlement point location data could further improve the built-up area
classification. Digital elevation features and pan-sharpening could, however, not significantly improve
the classification results. The study highlights the importance of dynamically tuned classifier
parameters, and underlines the use of Shannon entropy computed from the soft answers of the SVM as
a valid measure of the spatial distribution of classification uncertainties.

Abstract/Summary

 

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