TY - GEN
T1 - Surface object recognition with CNN and SVM in Landsat 8 images
AU - Ishii, Tomohiro
AU - Nakamura, Ryosuke
AU - Nakada, Hidemoto
AU - Mochizuki, Yoshihiko
AU - Ishikawa, Hiroshi
N1 - Publisher Copyright:
© 2015 MVA organization.
PY - 2015/7/8
Y1 - 2015/7/8
N2 - There is a series of earth observation satellites called Landsat, which send a very large amount of image data every day such that it is hard to analyze manually. Thus an effective application of machine learning techniques to automatically analyze such data is called for. In surface object recognition, which is one of the important applications of such data, the distribution of a specific object on the surface is surveyed. In this paper, we propose and compare two methods for surface object recognition, one using the convolutional neural network (CNN) and the other support vector machine (SVM). In our experiments, CNN showed higher performance than SVM. In addition, we observed that the number of negative samples have a influence on the performance, and it is necessary to select the number of them for practical use.
AB - There is a series of earth observation satellites called Landsat, which send a very large amount of image data every day such that it is hard to analyze manually. Thus an effective application of machine learning techniques to automatically analyze such data is called for. In surface object recognition, which is one of the important applications of such data, the distribution of a specific object on the surface is surveyed. In this paper, we propose and compare two methods for surface object recognition, one using the convolutional neural network (CNN) and the other support vector machine (SVM). In our experiments, CNN showed higher performance than SVM. In addition, we observed that the number of negative samples have a influence on the performance, and it is necessary to select the number of them for practical use.
UR - http://www.scopus.com/inward/record.url?scp=84941249240&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84941249240&partnerID=8YFLogxK
U2 - 10.1109/MVA.2015.7153200
DO - 10.1109/MVA.2015.7153200
M3 - Conference contribution
AN - SCOPUS:84941249240
T3 - Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015
SP - 341
EP - 344
BT - Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IAPR International Conference on Machine Vision Applications, MVA 2015
Y2 - 18 May 2015 through 22 May 2015
ER -