Extracting Urban Impervious Surface from WorldView-2 and Airborne LiDAR Data Using 3D Convolutional Neural Networks

Journal of the Indian Society of Remote Sensing

Volumn 47, Issue 3, 2019, Pages 401-412

  Sun, Zhongchang ^a,b   Zhao, Xiangwei ^c   Wu, Mengfan ^d   Wang, Cuizhen ^e  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b Key Laboratory of the Earth Observation of Hainan Province   (China)

^c SHANDONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^d BEIJING NORMAL UNIVERSITY   (China)

^e UNIVERSITY OF SOUTH CAROLINA   (United States)

Author keywords

Airborne light detection and ranging (LiDAR); Convolutional neural networks (CNNs); Impervious surface; Support vector machine (SVM); WorldView 2

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DATA SET; ENVIRONMENTAL IMPACT; IMAGE ANALYSIS; LIDAR; PIXEL; SATELLITE DATA; SATELLITE SENSOR; SUPPORT VECTOR MACHINE; THREE-DIMENSIONAL MODELING; URBAN AREA; URBANIZATION; WORLDVIEW;

EID: 85058113382     PISSN: 0255660X     EISSN: 09743006     Source Type: Journal    
DOI: 10.1007/s12524-018-0917-5     Document Type: Article

References (39)

1. Akpona, O., Frank, C., Sam, D. C., Jeroen, D., et al. (2018). Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities. Remote Sensing of Environment, 216, 482–496. 10.1016/j.rse.2018.07.011.
2. Chen, Y. S., Jiang, H. L., Li, C. Y., Jia, X. P., & Ghamisi, P. (2016a). Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 6232–6251. 10.14358/PERS.80.1.91.
3. Chen, S. Z., Wang, H. P., Xu, F., & Jin, Y. Q. (2016b). Target classification using the deep convolutional networks for SAR images. IEEE Transactions on Geoscience and Remote Sensing, 54(8), 4806–4817. 10.1109/TGRS.2016.2551720.
4. Cheng, G., Zhou, P. C., & Han, J. W. (2016). Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 54(12), 7405–7415. 10.1109/TGRS.2016.2601622.
5. Ding, J., Chen, B., Liu, H., & Huang, M. (2016). Convolutional neural network with data augmentation for SAR target recognition. IEEE Geoscience and Remote Sensing Letters, 13(3), 364–368. 10.1109/LGRS.2015.2513754.
6. Gao, Q., Lim, S., & Jia, X. (2018). Hyperspectral image classification using convolutional neural networks and multiple feature learning. Remote Sensing, 10, 1–18. 10.3390/rs10020299.
7. Guo, H. D., Yang, H. N., Sun, Z. C., Li, X. W., & Wang, C. Z. (2014). Synergistic use of optical and PolSAR imagery for urban impervious surface estimation. Photogrammetric Engineering and Remote Sensing, 80(1), 91–102. 10.14358/PERS.80.1.91.
8. Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507. 10.1126/science.1127647.
9. Hu, X., & Weng, Q. (2009). Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment, 113(10), 2089–2102. 10.1016/j.rse.2009.05.014.
10. Im, J. H., Lu, Z. Y., Rhee, J. Y., & Quackenbush, L. J. (2012). Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data. Remote Sensing of Environment, 117(1), 102–113. 10.1016/j.rse.2011.06.024.
11. Ji, S. W., Xu, W., Yang, M., & Yu, K. (2013). 3D convolutional neural networks for human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1), 221–231. 10.1109/TPAMI.2012.59.
12. Kim, H., Jeong, H., Jeon, J., & Bae, S. J. (2016). The impact of impervious surface on water quality and its threshold in Korea. Water, 8(4,), 1–9. 10.3390/w8040111.
13. Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778–782. 10.1109/LGRS.2017.2681128.
14. Längkvist, M., Kiselev, A., Alirezaie, M., & Loutfi, A. (2016). Classification and segmentation of satellite orthoimagery using convolutional neural networks. Remote Sensing, 8(4), 1–21. 10.3390/rs8040329.
15. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deeping learning. Nature, 521(7553), 436–444. 10.1038/nature14539.
16. Lin, T., Goyal, P., Girshick, R. B., He, K., & Doll, P. (2017). Focal loss for dense object detection. CoRR, abs/1708.02002, 1–10.
17. Ma, Q., Wu, J. G., & He, C. Y. (2016). A hierarchical analysis of the relationship between urban impervious surfaces and land surface temperatures: Spatial scale dependence, temporal variations, and bioclimatic modulation. Landscape Ecology, 31(5), 1139–1153. 10.1007/s10980-016-0356-z.
18. Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2016). Convolutional neural networks for large-scale remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 54(12), 7405–7415. 10.1109/TGRS.2016.2612821.
19. Marmanis, D., Datcu, M., Esch, T., & Stilla, U. (2016). Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geoscience and Remote Sensing Letters, 13(1), 105–109. 10.1109/LGRS.2015.2499239.
20. Parece, T. E., & Campbell, J. B. (2013a). Landsat and high resolution aerial photography. Remote Sensing, 5(10), 4942–4960. 10.3390/rs5104942.
21. Parece, T. E., & Campbell, J. B. (2013b). Comparing urban impervious surface identification using.
22. Scott, G. J., England, M. R., Starms, W. A., Marcum, R. A., & Davis, C. H. (2017). Training deep convolutional neural networks for land–cover classification of high-resolution imagery. IEEE Geoscience and Remote Sensing Letters, 14(4), 549–553. 10.1109/LGRS.2017.2657778.
23. Sevo, I., & Avramovic, A. (2016). Convolutional neural network based automatic object detection on aerial images. IEEE Geoscience and Remote Sensing Letters, 13(5), 740–744. 10.1109/LGRS.2016.2542358.
24. Sun, Z. C., Guo, H. D., Li, X. W., Lu, L. L., & Du, X. P. (2011). Estimating urban impervious surfaces from landsat-5 TM imagery using multilayer perceptron neural network and support vector machine. Journal of Applied Remote Sensing, 5(1), 053501. 10.1117/1.3539767.
25. Sun, Z. C., Li, X. U., Fu, W. X., Li, Y. K., & Tang, D. S. (2014). Long-term effects of land use/land cover change on surface runoff in urban areas of Beijing, China. Journal of Applied Remote Sensing, 8(1), 084596. 10.1117/1.JRS.8.084596.
26. Sun, Z. C., Wang, C. Z., Guo, H. D., & Shang, R. R. (2017). A modified normalized difference impervious surface index (MNDISI) for automatic urban mapping from landsat imagery. Remote Sensing, 9(9), 942. 10.3390/rs9090942.
27. Touchaei, A. G., Akbari, H., & Tessum., C. W. (2016). Effect of increasing urban albedo on meteorology and air quality of Montreal (Canada)—Episodic simulation of heat wave in 2005. Atmospheric Environment, 132(1), 188–206. 10.1016/j.atmosenv.2016.02.033.
28. Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M. (2015). Learning spatiotemporal features with 3D convolutional networks. In IEEE international conference on computer vision (ICCV), Chile, December 7–13.
29. Vedaldi, A. & Lenc, K. (2015). MatConvNet: Convolutional neural networks for MATLAB. In Proceedings of the 23rd ACM international conference on multimedia, Brisbane, Australia, 26–30 October 2015 (pp. 689–692). 10.1145/2733373.2807412.
30. Wang, J., Wu, Z., Wu, C., Cao, Z., et al. (2018). Improving impervious surface estimation: an integrated method of classification and regression trees (CART) and linear spectral mixture analysis (LSMA) based on error analysis. GIScience and Remote Sensing, 55(4), 583–603. 10.1080/15481603.2017.1417690.
31. Weber, C., Aguejdad, R., Briottet, X., et al. (2018). Hyperspectral imagery for environmental urban planning. In IGARSS 2018.
32. Xu, H. (2013). Rule-based impervious surface mapping using high spatial resolution imagery. International Journal of Remote Sensing, 34(1), 27–44. 10.1080/01431161.2012.703343.
33. Xu, J., Zhao, Y., Zhong, K., Zhang, F., Liu, X., & Sun, C. (2018a). Measuring spatio-temporal dynamics of impervious surface in Guangzhou, China, from 1988 to 2015, using time-series Landsat imagery. Science of the Total Environment, 627, 264–281. 10.1016/j.scitotenv.2018.01.155.
34. Xu, R., Zhang, H. S., & Lin, H. (2017). Urban impervious surfaces estimation from optical and SAR imagery: a comprehensive comparison. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9), 4010–4021. 10.1109/JSTARS.2017.2706747.
35. Xu, Z., Chen, J., Xia, J., Du, P., et al. (2018b). Multisource earth observation data for land-cover classification using random forest. IEEE Geoscience and Remote Sensing Letters, 15(5), 789–793. 10.1109/LGRS.2018.2806223.
36. Yue, Q., & Ma, C. (2016). Deep learning for hyperspectral data classification through exponential momentum deep convolution neural networks. Journal of Sensor, 2016, 1–9. 10.1155/2016/3150632.
37. Zhang, L., Zhang, M., & Yao, Y. (2018). Mapping seasonal impervious surface dynamics in Wuhan urban agglomeration, China from 2000 to 2016. International Journal of Applied Earth Observation and Geoinformation, 70, 51–61. 10.1016/j.jag.2018.04.005.
38. Zhang, P., Niu, X., Dou, Y., & Xia, F. (2017). Airport detection on optical satellite images using deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 14(8), 1183–1187. 10.1109/LGRS.2017.2673118.
39. Zhang, Y., Zhang, H., & Lin, H. (2014). Improving the impervious surface estimation with combined use of optical and SAR remote sensing images. Remote Sensing of Environment, 141(2), 155–167. 10.1016/j.rse.2013.10.028.