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.
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 (Presented at SPIE Remote Sensing: September 27, 2016; Published: 26 October 2016); https://doi.org/10.1117/12.2254825.
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