Journal of Advanced Navigation Technology (한국항행학회논문지)

The Korean Navigation Institute (한국항행학회)
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Aerial Scene Labeling Based on Convolutional Neural Networks

Convolutional Neural Networks기반 항공영상 영역분할 및 분류

  • 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)
  • 나종필 (한국항공대학교 정보통신공학과) ;
  • 황승준 (한국항공대학교 정보통신공학과) ;
  • 박승제 (한국항공대학교 정보통신공학과) ;
  • 백중환 (한국항공대학교 정보통신공학과)
  • Received : 2015.10.26
  • Accepted : 2015.12.26
  • Published : 2015.12.30
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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.

항공영상은 디지털 광학 영상 기술의 성장과 무인기(UAV)의 발달로 인하여 영상의 도입 및 공급이 크게 증가하였고, 이러한 항공영상 데이터를 기반으로 지상의 속성 추출, 분류, 변화탐지, 영상 융합, 지도 제작 형태로 활용되고 있다. 특히, 영상분석 및 활용에 있어 딥 러닝 알고리즘은 패턴인식 분야의 한계를 극복하는 새로운 패러다임을 보여주고 있다. 본 논문은 딥 러닝 알고리즘인 ConvNet기반으로 항공영상의 영역분할 및 분류 결과를 통한 더욱더 넓은 범위와 다양한 분야에 적용할 수 있는 가능성을 제시한다. 학습데이터는 도로, 건물, 평지, 숲 총 3000개 4-클래스로 구축하였고 클래스 별로 일정한 패턴을 가지고 있어 특징 벡터맵을 통한 결과가 서로 다르게 나옴을 확인할 수 있다. 본 연구의 알고리즘은 크게 두 가지로 구성 되어 있는데 특징추출은 ConvNet기반으로 2개의 층을 쌓았고, 분류 및 학습과정으로 다층 퍼셉트론과 로지스틱회귀 알고리즘을 활용하여 특징들을 분류 및 학습시켰다.

Keywords

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