Abstract
Abstract. The objective of this project was to compare two non-parametric classification methods (“Support Vector Machine” – SVM and “Artificial Neural Networks” – ANN) of road regions in high spatial resolution images and associated with data from Airborne Laser Scanning. The study aims to verify what kind of influence the layers of attributes have on the performance from respective classifiers: SVM and RNA. Our method based on tests of this classifiers on 4 bands of airborne images and normalization of the digital surface model (DSM) for showing only information on objects height in relation to ground and not of these in relation to the ground and relief, generating band 5. The samples were used to train chosen non-parametric classifiers (training sets for each different input image/landscape). All classifications had the same set of training samples and the same classification parameters. The optimal parameters for classifications were obtained through the existing library in the Weka mining package: LibSVM and LibMultiLayerPerceptron. Our results demonstrated the existence of a direct relationship between the elevation band of the targets in relation to the terrain (band 05) with the improvement of their performance and lower degree of between bands correlation can also be considered a factor that has a positive influence. As for Neural Networks, the experiment results demonstrate that the presence of the near infrared band (band 04) was decisive for the performance improving of certain combinations in relation to others.
Highlights
The objective of the project was to compare two non-parametric classification methods (“Support Vector Machine” - SVM and “Artificial Neural Networks” - ANN) of road regions in extremely high spatial resolution images
Initially, it was subtracted from Digital Surface Model (DSM) of the portion corresponding to the Digital Terrain Model (DTM)
The algorithms chosen for the experiments were: Support Vector Machine (SVM) and Neural Net or Artificial Neural Network (ANN), both implemented
Summary
The objective of the project was to compare two non-parametric classification methods (“Support Vector Machine” - SVM and “Artificial Neural Networks” - ANN) of road regions in extremely high spatial resolution images. This word use data from “Airborne Laser Scanning” – ALS associated with multispectral data. The intense dynamics of use and occupation of spaces in large urban areas in developing countries generate an accelerated process of transformations in land use of these cities This scenario demands efforts to map new occupied or altered spaces and new roads created (regular or not) or, still, the necessary updating maps (MABOUDI,2017)
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