Browse > Article
http://dx.doi.org/10.7780/kjrs.2017.33.6.2.3

Deep Learning-based Hyperspectral Image Classification with Application to Environmental Geographic Information Systems  

Song, Ahram (Department of Civil Environmental Engineering, Seoul National University)
Kim, Yongil (Department of Civil Environmental Engineering, Seoul National University)
Publication Information
Korean Journal of Remote Sensing / v.33, no.6_2, 2017 , pp. 1061-1073 More about this Journal
Abstract
In this study, images were classified using convolutional neural network (CNN) - a deep learning technique - to investigate the feasibility of information production through a combination of artificial intelligence and spatial data. CNN determines kernel attributes based on a classification criterion and extracts information from feature maps to classify each pixel. In this study, a CNN network was constructed to classify materials with similar spectral characteristics and attribute information; this is difficult to achieve by conventional image processing techniques. A Compact Airborne Spectrographic Imager(CASI) and an Airborne Imaging Spectrometer for Application (AISA) were used on the following three study sites to test this method: Site 1, Site 2, and Site 3. Site 1 and Site 2 were agricultural lands covered in various crops,such as potato, onion, and rice. Site 3 included different buildings,such as single and joint residential facilities. Results indicated that the classification of crop species at Site 1 and Site 2 using this method yielded accuracies of 96% and 99%, respectively. At Site 3, the designation of buildings according to their purpose yielded an accuracy of 96%. Using a combination of existing land cover maps and spatial data, we propose a thematic environmental map that provides seasonal crop types and facilitates the creation of a land cover map.
Keywords
Deep Learning; Convolutional Neural Network; Hyperspectral image; Classification;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Cao, X., F. Zhou, L. Xu, D. Meng, Z. Xu, and J. Paisley, 2017. Hyperspectral Image Segmentation with Markov Random Fields and a Convolutional Neural Network, Computer Vision and Pattern Recognition, arXiv preprint arXiv:1705.00727.
2 Duro, D., S. Franklin, and M. Dube, 2012. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG Imagery, Remote Sensing of Environment, 118: 259-272.   DOI
3 LeCun, Y., Y. Bengio, and G. Hinton, 2015. Deep learning, Nature, 521(7553): 436-444.   DOI
4 Lee, H., R. Kang, K. Kim, G. Nam, M. Kwon, H. Song, S. Cheon, J. Lee, J. Yoon, I. Lee, and H. Lee, 2013. Estimating temporal and spatial variation of chlorophyll-a concentration from multi-spectral imagery in Nak-dong River basin, Water Quality Control Center, NEIR-RP2013-296 (in Korean with English abstract).
5 Li, Y., H. Zhang, and Q. Shen, 2017. Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network, Remote Sensing, 9(1): 67.   DOI
6 Krizhevsky, A., I. Sutskever, and G. Hinton, 2012. Imagenet classification with deep convolutional neural networks, In Advances in neural information processing systems, 1097-1105.
7 Kim, H. and J. Yeom, 2012. A study on object-based image analysis methods for land cover classification in agricultural areas, Journal of the Korean Association of Geographic Information Studies, 15(4): 26-41 (in Korean with English abstract).   DOI
8 Makantasis, K., K. Karantzalos, A. Doulamis, and N. Doulamis, 2015. Deep supervised learning for hyperspectral data classification through convolutional neural networks, Proc. of 2015 In Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, Jul. 26-31, pp. 4959-4962.
9 Ministry of Environment, 2016. Build a land cover map [7th] and improve the national environmental guidance system (in Korean with English abstract).
10 Petersson, H., D. Gustafsson, and D. Bergstrom, 2016. Hyperspectral image analysis using deep learning -A review. In Image Processing Theory Tools and Applications (IPTA), Proc. of 2016 6th International Conference, Oulu, Finland, Dec. 12-15, pp. 1-6.
11 Shine, J., T. Lee, P. Jung, and H. Kwon, 2015. A study on land cover map of UAV imagery using an object-based classification method, Journal of the Korean Society for Geospatial Information Science, 23(4): 25-33 (in Korean with English abstract).   DOI
12 Yu, S., S. Jia, and C. Xu, 2017. Convolutional neural networks for hyperspectral image classification, Neurocomputing, 219: 88-98.   DOI