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http://dx.doi.org/10.7848/ksgpc.2019.37.6.435

Semantic Classification of DSM Using Convolutional Neural Network Based Deep Learning  

Lee, Dae Geon (Geo& Lab, Geo& Co., Ltd.)
Cho, Eun Ji (Dept. of Environment, Energy & Geoinformatics, Sejong University)
Lee, Dong-Cheon (Dept. of Environment, Energy & Geoinformatics, Sejong University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.37, no.6, 2019 , pp. 435-444 More about this Journal
Abstract
Recently, DL (Deep Learning) has been rapidly applied in various fields. In particular, classification and object recognition from images are major tasks in computer vision. Most of the DL utilizing imagery is primarily based on the CNN (Convolutional Neural Network) and improving performance of the DL model is main issue. While most CNNs are involve with images for training data, this paper aims to classify and recognize objects using DSM (Digital Surface Model), and slope and aspect information derived from the DSM instead of images. The DSM data sets used in the experiment were established by DGPF (German Society for Photogrammetry, Remote Sensing and Geoinformatics) and provided by ISPRS (International Society for Photogrammetry and Remote Sensing). The CNN-based SegNet model, that is evaluated as having excellent efficiency and performance, was used to train the data sets. In addition, this paper proposed a scheme for training data generation efficiently from the limited number of data. The results demonstrated DSM and derived data could be feasible for semantic classification with desirable accuracy using DL.
Keywords
CNN; DL Model; Training and Validation Data; DSM Classification;
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