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http://dx.doi.org/10.12673/jant.2015.19.6.484

Aerial Scene Labeling Based on Convolutional Neural Networks  

Na, Jong-Pil (Information & Telecommunication Engineering, Korea Aerospace University)
Hwang, Seung-Jun (Information & Telecommunication Engineering, Korea Aerospace University)
Park, Seung-Je (Information & Telecommunication Engineering, Korea Aerospace University)
Baek, Joong-Hwan (Information & Telecommunication Engineering, Korea Aerospace University)
Publication Information
Journal of Advanced Navigation Technology / v.19, no.6, 2015 , pp. 484-491 More about this Journal
Abstract
Aerial scene is greatly increased by the introduction and supply of the image due to the growth of digital optical imaging technology and development of the UAV. It has been used as the extraction of ground properties, classification, change detection, image fusion and mapping based on the aerial image. In particular, in the image analysis and utilization of deep learning algorithm it has shown a new paradigm to overcome the limitation of the field of pattern recognition. This paper presents the possibility to apply a more wide range and various fields through the segmentation and classification of aerial scene based on the Deep learning(ConvNet). We build 4-classes image database consists of Road, Building, Yard, Forest total 3000. Each of the classes has a certain pattern, the results with feature vector map come out differently. Our system consists of feature extraction, classification and training. Feature extraction is built up of two layers based on ConvNet. And then, it is classified by using the Multilayer perceptron and Logistic regression, the algorithm as a classification process.
Keywords
Convolutional neural networks; ConvNet; Deep learning; Image segmentation; Scene labeling;
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Times Cited By KSCI : 1  (Citation Analysis)
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