한국인터넷방송통신학회논문지 (The Journal of the Institute of Internet, Broadcasting and Communication)

  • 제17권5호
  • /
  • Pages.97-102
  • /
  • 2017
  • /
  • 2289-0238(pISSN)
  • /
  • 2289-0246(eISSN)
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
권호 표지
DOI QR코드

DOI QR Code

컨볼루션 신경망을 기반으로 한 드론 영상 분류

Drone Image Classification based on Convolutional Neural Networks

  • 주영도 (강남대학교 컴퓨터미디어정보공학부)
  • Joo, Young-Do (Dept. of Computer & Media Information, Kangnam University)
  • 투고 : 2017.08.14
  • 심사 : 2017.10.13
  • 발행 : 2017.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

최근 고해상도 원격탐사 자료의 분류방안으로 컨볼루션 신경망(Convolutional Neural Networks)을 비롯한 딥 러닝 기법들이 소개되고 있다. 본 논문에서는 드론으로 촬영된 농경지 영상의 작물 분류를 위해 컨볼루션 신경망을 적용하여 가능성을 검토하였다. 농경지를 논, 고구마, 고추, 옥수수, 깻잎, 과수, 비닐하우스로 총 7가지 클래스로 나누고 수동으로 라벨링 작업을 완료했다. 컨볼루션 신경망 적용을 위해 영상 전처리와 정규화 작업을 수행하였으며 영상분류 결과 98%이상 높은 정확도를 확인할 수 있었다. 본 논문을 통해 기존 영상분류 방법들에서 딥 러닝 기반 영상분류 방법으로의 전환이 빠르게 진행될 것으로 예상되며, 그 성공 가능성을 확신할 수 있었다.

Recently deep learning techniques such as convolutional neural networks (CNN) have been introduced to classify high-resolution remote sensing data. In this paper, we investigated the possibility of applying CNN to crop classification of farmland images captured by drones. The farming area was divided into seven classes: rice field, sweet potato, red pepper, corn, sesame leaf, fruit tree, and vinyl greenhouse. We performed image pre-processing and normalization to apply CNN, and the accuracy of image classification was more than 98%. With the output of this study, it is expected that the transition from the existing image classification methods to the deep learning based image classification methods will be facilitated in a fast manner, and the possibility of success can be confirmed.

키워드

참고문헌

  1. M. Langkvist, A. Kiselev, M. Alirezaie, and A. Loutfi, "Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks", Remote Sensing, Vol. 8, pp. 329-350, 2016. DOI: 10.3390/rs8040329
  2. G. Fu, C. Liu, R. Zhou, T. Sun, and Q. Zhang, "Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network", Remote Sensing, Vol. 9, pp. 498-519, 2017. DOI: 10.3390/rs9050498
  3. M. Castelluccio, G. Poggi, C. Sansone, and L. Verdoliva, "Land Use Classification in Remote Sensing Images by Convolutional Neural Networks", Available online: https://arxiv.org/abs/1508.00092(accessed on 12 April 2016).
  4. D. Choi and J. Park, "A Study on Security Authentication Vector Generation of Virtualized Internal Environment using Machine Learning Algorithm", The Journal of the Institute of Internet Broadcasting and Communication(JIIBC), Vol. 16, No. 6, pp. 33-42, 2016. DOI: https://doi.org/10.7236/JIIBC.2016.16.6.33
  5. A. Karpathy, "Neural Networks Part 1: Setting Up the Architecture." Notes for CS231n Convolutional Neural Networks for Visual Recognition, Stanford University, https://cs231n.github.io/neural-networks -1/, 2017.
  6. E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, "Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification", IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, Issue. 2, pp. 645-657, 2017. DOI: 10.1109/TGRS.2016.2612821
  7. Deep Learning, Available online: https://en.wikipedia.org/wiki/Deep_learning (accessed on 3 May 2017).
  8. Y. Zhong, F. Fei, Y. Liu, B. Zhao, H. Jiao and L. Zhang, "SatCNN: Satellite Image Dataset Classification using Agile Convolutional Neural Networks", Remote Sensing Letters, Vol. 8, Issue 2, pp. 136-145, 2017. DOI: https://dx.doi.org/10.1080/2150704X.2016.1235299
  9. R. Socher, B. Huval, B. Bath, C. D. Manning, and A. Y. Ng, "Convolutional-recursive Deep Learning for 3D Object Classification", Advances in Neural Information Processing Systems; Curran Associates: North Miami Beach, FL, USA, pp. 665-673, 2012.
  10. Z. Zhang, "A Flexible New Technique for Camera Calibration", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, Issue. 11, pp. 1330-1334, 2000. DOI: 10.1109/34.888718
  11. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet Classification with Deep Convolutional Neural Network", Advances in Neural Information Processing Systems 25, pp. 1106-1114, 2012.