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Variable selection for road segmentation in aerial images

 
: Warnke, Sven; Bulatov, Dimitri

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Volltext urn:nbn:de:0011-n-4558344 (1.7 MByte PDF)
MD5 Fingerprint: 89f4d72f03cf8398aa3fd3f66f5a1432
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Erstellt am: 10.10.2017


Heipke, C. ; International Society for Photogrammetry and Remote Sensing -ISPRS-:
ISPRS Hannover Workshop 2017 : HRIGI 17 - CMRT 17 - ISA 17 - EuroCOW 17, 6-9 June 2017, Hannover, Germany
Istanbul: ISPRS, 2017 (ISPRS Archives XLII-1/W1)
S.297-304
Hannover Workshop "High-Resolution Earth Imaging for Geospatial Information" (HRIGI) <2017, Hannover>
European Calibration and Orientation Workshop (EuroCOW) <2017, Hannover>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
classification; feature selection; logistic regression; random forest; road extraction

Abstract
For extraction of road pixels from combined image and elevation data, Wegner et al. (2015) proposed classification of superpixels into road and non-road, after which a refinement of the classification results using minimum cost paths and non-local optimization methods took place. We believed that the variable set used for classification was to a certain extent suboptimal, because many variables were redundant while several features known as useful in Photogrammetry and Remote Sensing are missed. This motivated us to implement a variable selection approach which builds a model for classification using portions of training data and subsets of features, evaluates this model, updates the feature set, and terminates when a stopping criterion is satisfied. The choice of classifier is flexible; however, we tested the approach with Logistic Regression and Random Forests, and taylored the evaluation module to the chosen classifier. To guarantee a fair comparison, we kept the segment-based approach and most of the variables from the related work, but we extended them by additional, mostly higher-level features. Applying these superior features, removing the redundant ones, as well as using more accurately acquired 3D data allowed to keep stable or even to reduce the misclassification error in a challenging dataset.

: http://publica.fraunhofer.de/dokumente/N-455834.html