Building block extraction and classification by means of Markov random fields using aerial imagery and LiDAR data

E. Bratsolis; M. Sigelle; E. Charou

doi:10.1117/12.2254825

26 October 2016 Building block extraction and classification by means of Markov random fields using aerial imagery and LiDAR data

E. Bratsolis, M. Sigelle, E. Charou

Author Affiliations +

Proceedings Volume 10008, Remote Sensing Technologies and Applications in Urban Environments; 100080Q (2016) https://doi.org/10.1117/12.2254825
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

Abstract

Building detection has been a prominent area in the area of image classification. Most of the research effort is adapted to the specific application requirements and available datasets. Our dataset includes aerial orthophotos (with spatial resolution 20cm), a DSM generated from LiDAR (with spatial resolution 1m and elevation resolution 20 cm) and DTM (spatial resolution 2m) from an area of Athens, Greece. Our aim is to classify these data by means of Markov Random Fields (MRFs) in a Bayesian framework for building block extraction and perform a comparative analysis with other supervised classification techniques namely Feed Forward Neural Net (FFNN), Cascade-Correlation Neural Network (CCNN), Learning Vector Quantization (LVQ) and Support Vector Machines (SVM). We evaluated the performance of each method using a subset of the test area. We present the classified images, and statistical measures (confusion matrix, kappa coefficient and overall accuracy). Our results demonstrate that the MRFs and FFNN perform better than the other methods.

Conference Presentation

Citation Download Citation

E. Bratsolis, M. Sigelle, and E. Charou "Building block extraction and classification by means of Markov random fields using aerial imagery and LiDAR data", Proc. SPIE 10008, Remote Sensing Technologies and Applications in Urban Environments, 100080Q (26 October 2016); https://doi.org/10.1117/12.2254825

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available