Advanced SearchSearch Tips
Aerial Scene Labeling Based on Convolutional Neural Networks
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Aerial Scene Labeling Based on Convolutional Neural Networks
Na, Jong-Pil; Hwang, Seung-Jun; Park, Seung-Je; Baek, Joong-Hwan;
  PDF(new window)
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.
Convolutional neural networks;ConvNet;Deep learning;Image segmentation;Scene labeling;
 Cited by
C. H. Yeol and M. Y. Hong, “Introduction and key issues of deep learning,” Korea Information Processing Society, Vol. 22, No. 1, pp. 7-21, 2015.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, Vol. 86, No. 11, pp. 2278-2324, Nov. 1998. crossref(new window)

A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, pp. 1097-1105. 2012.

C. Zhang, Cha, and Z. Zhang, "Improving multiview face detection with multi-task deep convolutional neural networks," in Applications of Computer Vision, IEEE Conference on, Steamboat Springs; CO, pp. 1036-1041, 2014.

D. Ciresan, U. Meier, and J. Schmidhuber, "Multi-column deep neural networks for image classification," in IEEE Winter Conference on Computer Vision and Pattern Recognition, Providence: RI, pp. 3642-3649, 2012.

L. Deng, D. Yu, “Deep learning: methods and applications,” Foundations and Trends in Signal Processing, Vol. 7, No. 3-4, pp. 197-387, 2014. crossref(new window)

C. Farabet, C. Couprie, L. Najman, and Y. LeCun, "Learning hierarchical features for scene labeling," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35 No. 8, pp. 1915-1929, 2013.

A. M. Cheriyadat, “Unsupervised feature learning for aerial scene classification,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 1, pp. 439-451, 2014. crossref(new window)

I. K. Kim, S. J. Hwang, J. P. Na, S. J. Park, and J. H. Baek, “Super-pixel-based segmentation and classification for UAV image,” The Journal of Korea Navigation Institute, Vol. 18, No. 2, pp. 151-157, Apr. 2014.

H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, “Speeded up robust features (SURF),” Computer Vision and Image Understanding, Vol. 110, No. 3, pp. 346-359, 2007.

D. G. Lowe, "Object recognition from local scale-invariant features," in The Proceedings of the Seventh IEEE International Conference on, Kerkyra: Greece, Vol. 2, pp. 1150-1157, 1999.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, "Slic superpixels," EPFL-REPORT-149300, June. 2010.

M. W. Gardner, and S. R. Dorling, “Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric Sciences,” Atmospheric Environment, Vol. 32, No. 14, pp. 2627-2636, 1998. crossref(new window)

D. R. Cox, “The regression analysis of binary sequences,” Journal of the Royal Statistical Society. Series B (Methodological), Vol. 20, No. 2, pp. 215-242, 1958.

D. W. Hosmer, S. Lemeshow, E. D. Cook, Applied Logistic Regression, 2nd edition, New York, NY: John Wiley & Sons, 2000.

P. F. Felzenszwalb, and D. P. Huttenlocher, “Efficient graph-based image segmentation,” International Journal of Computer Vision, Vol. 59, No. 2, pp. 167-181, 2004. crossref(new window)