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.
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.
KEYWORDS: RGB color model, 3D modeling, Orthophoto maps, Spatial resolution, Image fusion, LIDAR, Image resolution, Data fusion, Data acquisition, Data modeling
Nowadays there is an increasing demand for detailed 3D modeling of buildings using elevation data such as those
acquired from LiDAR airborne scanners. The various techniques that have been developed for this purpose typically
perform segmentation into homogeneous regions followed by boundary extraction and are based on some combination of
LiDAR data, digital maps, satellite images and aerial orthophotographs. In the present work, our dataset includes an
aerial RGB orthophoto, a DSM and a DTM with spatial resolutions of 20cm, 1m and 2m respectively. Next, a
normalized DSM (nDSM) is generated and fused with the optical data in order to increase its resolution to 20cm. The
proposed methodology can be described as a two-step approach. First, a nearest neighbor interpolation is applied on the
low resolution nDSM to obtain a low quality, ragged, elevation image. Next, we performed a mean shift-based
discontinuity preserving smoothing on the fused data. The outcome is on the one hand a more homogeneous RGB image,
with smoothed terrace coloring while at the same time preserving the optical edges and on the other hand an upsampled
elevation data with considerable improvement regarding region filling and “straightness” of elevation discontinuities.
Besides the apparent visual assessment of the increased accuracy of building boundaries, the effectiveness of the
proposed method is demonstrated using the processed dataset as input to five supervised classification methods. The
performance of each method is evaluated using a subset of the test area as ground truth. Comparisons with classification
results obtained with the original data demonstrate that preprocessing the input dataset using the mean shift algorithm
improves significantly the performance of all tested classifiers for building block extraction.
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