Browse > Article
http://dx.doi.org/10.7848/ksgpc.2021.39.6.409

Comparative Experiment of Cloud Classification and Detection of Aerial Image by Deep Learning  

Song, Junyoung (Dept. of Advanced Technology Fusion, Konkuk University)
Won, Taeyeon (Dept. of Advanced Technology Fusion, Konkuk University)
Jo, Su Min (Dept. of Advanced Technology Fusion, Konkuk University)
Eo, Yang Dam (Dept. of Civil and Environmental Engineering, Konkuk University)
Park, So young (Geographic Information Division, National Geographic Information Institute, Ministry of Land, Infrastructure and Transport)
Shin, Sang ho (Geographic Information Division, National Geographic Information Institute, Ministry of Land, Infrastructure and Transport)
Park, Jin Sue (Project Development Division, ALLforLAND.Co.Ltd)
Kim, Changjae (Dept. of Civil and Environmental Engineering, Myongji University,)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.39, no.6, 2021 , pp. 409-418 More about this Journal
Abstract
As the amount of construction for aerial photography increases, the need for automation of quality inspection is emerging. In this study, an experiment was performed to classify or detect clouds in aerial photos using deep learning techniques. Also, classification and detection were performed by including satellite images in the learning data. As algorithms used in the experiment, GoogLeNet, VGG16, Faster R-CNN and YOLOv3 were applied and the results were compared. In addition, considering the practical limitations of securing erroneous images including clouds in aerial images, we also analyzed whether additional learning of satellite images affects classification and detection accuracy in comparison a training dataset that only contains aerial images. As results, the GoogLeNet and YOLOv3 algorithms showed relatively superior accuracy in cloud classification and detection of aerial images, respectively. GoogLeNet showed producer's accuracy of 83.8% for cloud and YOLOv3 showed producer's accuracy of 84.0% for cloud. And, the addition of satellite image learning data showed that it can be applied as an alternative when there is a lack of aerial image data.
Keywords
Deep Learning; Classification; Detection; GoogLeNet; VGG16; Faster R-CNN; YOLOv3;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Song, H.J., Lee, E.B., Jo, H.J., Park, S.Y., Kim, S.Y., Kim, H.J., and Hong, J.W. (2020), Evaluation of Classification and Accuracy in Chest X-ray Images using Deep Learning with Convolution Neural Network, Journal of the Korean Society of Radiology, Vol. 14, No. 1, pp. 39-44. (in Korean with English abstract)   DOI
2 Zeiler, M.D., and Fergus, R. (2014), Visualizing and understanding convolutional networks, In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.), ECCV 2014, Springer, Cham, vol. 8689, pp. 818-833.
3 Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014), Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 24-27 June, Columbus, Ohio, pp. 580-587.
4 He, K., Zhang, X., Ren, S., and Sun, J. (2015), Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, IEEE International Conference on Computer Vision (ICCV), 13-16 December, Santiago, Chile, pp. 1026-1034.
5 Kim, B.H., Kim, Y.H., Han, Y.K., Choi, W.S., and Kim, Y.I. (2014), Fully Automated Generation of Cloud-free Imagery Using Landsat-8, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 32, No. 2, pp. 133-142. (in Korean with English abstract)   DOI
6 Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012), ImageNet classification with deep convolutional neural networks, In Proceedings of NIPS, pp. 1106-1114.
7 Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A. C. 2016, SSD: Single Shot MultiBox Detector, In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.), Computer Vision - ECCV 2016, Springer, Cham. pp. 21-37.
8 Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998), Gradient-based learning applied to document recognition, IEEE, vol. 86, no. 11, pp. 2278-2324.   DOI
9 Cho, W., Lee, S.H., Choi, S.S., Hwang, H.D., and Lee, H.J. (2003), Quality Improvement on Aerial Photographs, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 21, No. 3, pp. 277-287. (in Korean with English abstract)
10 Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollar, P. 2017, Focal Loss for Dense Object Detection, IEEE International Conference on Computer Vision (ICCV), 2017, 22-29 October, Venice, Italy, pp. 2980-2988.
11 Redmon, J., and Farhadi, A. (2018), YOLOv3: An Incremental Improvement, arXiv, https://arxiv.org/abs/1804.02767 (last date accessed: 17 December 2021).
12 Liu, Z., Du, J., Tian, F., and Wen, J. (2019), MR-CNN: A Multi-Scale Region-Based Convolutional Neural Network for Small Traffic Sign Recognition, IEEE Access, vol. 7, pp. 57120-57128.   DOI
13 Mittal, A., Moorthy, A.K., and Bovik, A.C. (2012), No-Reference Image Quality Assessment in the Spatial Domain, IEEE TRANSACTIONS ON IMAGE PROCESSING, Vol. 21, No. 12, pp. 4695-4708.   DOI
14 Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016), You Only Look Once: Unified, Real-Time Object Detection, 29th IEEE Conference on Computer Vision and Pattern Recognition, 27-30 June, Las Vegas, Nevada, USA, pp. 779-788.
15 Ren, S., He, K., Girshick, R., and Sun, J. (2015), Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 6, pp. 1137-1149.   DOI
16 Seong, S.K., Choi, S.K., and Choi, J.W., Cloud Detection of PlanetScope Imagery Based on Deeplab-V3+ by Using Transfer Learning, Journal of Korean Society for Geospatial Information Science, Vol. 28, No. 4, pp. 25-32. (in Korean with English abstract)   DOI
17 Lee, J.O., and Sung, S.M. (2019), Quality Evaluation of UAV Images Using Resolution Target, Journal of the Korean Association of Geographic Information Studies, Vol. 22, No. 1, pp. 103-113. (in Korean with English abstract)   DOI
18 National Geographic Information Institute (2015), Data Quality for Aerial Photograph, NGII-STD.2015-7/2020, National Geographic Information Institute, Suwon, Korea.
19 Kim, B., Kim, G., Jin, S., and Cho, S. (2019), A Comparative Study on Performance of Deep Learning Models for Vision-based Concrete Crack Detection according to Model Types, Journal of the Korean Society of Safety, Vol. 34, No. 6, pp. 50-57. (in Korean with English abstract)   DOI
20 He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017), Mask R-CNN, IEEE International Conference on Computer Vision (ICCV), 22-29 October, Venice, Italy, pp. 2961-2969.
21 Lee, M.H., Cheon, E.J., and Eo, Y.D. (2019), Cloud Detection and Restoration of Landsat-8 using STARFM, Korean Journal of Remote Sensing, Vol. 35, No. 5-2, pp. 861-871. (in Korean with English abstract)
22 Simonyan, K. and Zisserman, A. (2015), Very Deep Convolutional Networks For Large-Scale Image Recognition, Computer Vision and Pattern Recognition, Cornell University, Ithaca, NY, USA.
23 Sun, L., Mi, X., Wei, J., Wang, J., Tian, X., Yu, H., and Gan, P. (2017), A cloud detection algorithm generating method for remote sensing data at visible to short-wave infrared wavelengths, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 124, pp. 70-88.   DOI
24 Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015), Going Deeper with Convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition, 7-12 June, Boston, MA, USA, pp. 1-9.
25 Zhang, H., Li, B., Zhang, J., and Xu, F. (2014), Aerial Image Series Quality Assessment, 35th International Symposium on Remote Sensing of Environment-2013, 22-26 April, Beijing, China, Vol. 17, pp. 012183.
26 Zhang, G.F., Wang, L., Zheng, Z.N., Chen, Y.F., Zhou, Z.B., and Zhao K. (2016), No-reference aerial image quality assessment based on natural scene statistics and color correlation blur metric, 13th International Conference on Transmission & Distribution Construction, Operation & Live-Line Maintenance, 21 August, Columbus, OH, USA, pp. 1-4.