지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
SVM (Support vector machine) analysis was performed after applying a deep learning technique based on an Inception-based model (GoogLeNet). The accuracy of automatic image classification was analyzed using an SVM with multiple virtual grid sizes. Six classes were selected from a standard land cover map. Cars were added as a separate item to increase the classification accuracy of roads. The virtual grid size was 2–5 m for natural areas, 5–10 m for traffic areas, and 10–15 m for building areas, based on the size of items and the resolution of input images. The results demonstrate that automatic classification accuracy can be increased by adopting an integrated approach that utilizes weighted virtual grid sizes for different classes.
목차
Abstract
1. Introduction
2. Proposed Methodology
3. Experimental Data and Training
4. Results and Discussion
5. Conclusion and Future Work
References
참고문헌 (16)
참고문헌 신청
Song, A.R. (2019), A Novel Deep learning Framework for Multi-Class Change Detection of Hyperspectral Images, Ph.D. Dissertation, Seoul National University, 13p. (in Korean with English abstract)
Song, A.R. and Kim, Y.I. (2017), Deep Learning-based Hyperspectral Image Classification with Application to Environmental Geographic Information Systems, Korean Journal of Remote Sensing, Vol.33, No.6-2, pp.1061~1073. (in Korean with English abstract)
Joo, Y.D. (2017), Drone Image Classification based on Convolutional Neural Networks, The Journal of The Institute of Internet, Broadcasting and Communication (IIBC) , Vol. 17, No. 5, pp.97-102. (in Korean with English abstract)
Jo, H.J. (2017), A Study on Image Acquisition and Image Processing using Drones, Master thesis, Kyungpook University, 83p. (in Korean with English abstract)
Bengio, Y. (2011), Deep learning of representations for unsupervised and transfer learning, Proceeding of ICML workshop on unsupervised and transfer learning-2011, 02 July, Bellevue, Washington, USA Vol. 27, pp.17-36.
Song, A.R. and Kim, Y.I. (2017), Deep Learning-based Hyperspectral Image Classification with Application to Environmental Geographic Information Systems, Korean Journal of Remote Sensing, Vol.33, No.6-2, pp.1061~1073. (in Korean with English abstract)
Joo, Y.D. (2017), Drone Image Classification based on Convolutional Neural Networks, The Journal of The Institute of Internet, Broadcasting and Communication (IIBC) , Vol. 17, No. 5, pp.97-102. (in Korean with English abstract)
Jo, H.J. (2017), A Study on Image Acquisition and Image Processing using Drones, Master thesis, Kyungpook University, 83p. (in Korean with English abstract)
Bengio, Y. (2011), Deep learning of representations for unsupervised and transfer learning, Proceeding of ICML workshop on unsupervised and transfer learning-2011, 02 July, Bellevue, Washington, USA Vol. 27, pp.17-36.
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