17 October 2017 Building block extraction and classification by means of aerial images fused with super-resolution reconstructed elevation data
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Abstract
The detailed three-dimensional modeling of buildings utilizing elevation data, such as those provided by light detection and ranging (LiDAR) airborne scanners, is increasingly demanded today. There are certain application requirements and available datasets to which any research effort has to be adapted. Our dataset includes aerial orthophotos, with a spatial resolution 20 cm, and a digital surface model generated from LiDAR, with a spatial resolution 1 m and an elevation resolution 20 cm, from an area of Athens, Greece. The aerial images are fused with LiDAR, and we classify these data with a multilayer feedforward neural network for building block extraction. The innovation of our approach lies in the preprocessing step in which the original LiDAR data are super-resolution (SR) reconstructed by means of a stochastic regularized technique before their fusion with the aerial images takes place. The Lorentzian estimator combined with the bilateral total variation regularization performs the SR reconstruction. We evaluate the performance of our approach against that of fusing unprocessed LiDAR data with aerial images. We present the classified images and the statistical measures confusion matrix, kappa coefficient, and overall accuracy. The results demonstrate that our approach predominates over that of fusing unprocessed LiDAR data with aerial images.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Antigoni Panagiotopoulou, Emmanuel Bratsolis, Eleni Charou, Stavros Perantonis, "Building block extraction and classification by means of aerial images fused with super-resolution reconstructed elevation data," Journal of Applied Remote Sensing 11(4), 045004 (17 October 2017). https://doi.org/10.1117/1.JRS.11.045004 . Submission: Received: 25 May 2017; Accepted: 28 September 2017
Received: 25 May 2017; Accepted: 28 September 2017; Published: 17 October 2017
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