A Deep Learning-based Classification method for Land Cover Monitoring Using UAV Images
Subject Areas : Journal of Radar and Optical Remote Sensing and GISHoda Yazdanparast 1 , Seyyed Reza Mousavi 2 , Ladan Ebadi 3 , Salar Mirzapour 4
1 - Faculty of Computer and IT Engineering, Amirkabir university, Tehran, Iran
2 - Depertment of Geomorphology, Islamic University of Nour, Mazandaran, Iran.
3 - Faculty Member of Mapping Engineering Department, Golestan University, Gorgan, Iran
4 - Department of Geographic Information System, Science and Research branch, Islamic Azad University, Tehran, Iran
Keywords: Deep Learning, Land Cover, CNN, UAV Image, Classification.,
Abstract :
A land cover map stands as a cornerstone of urban planning endeavors, furnishing indispensable insights into the landscape's composition and distribution. However, traditional methodologies for map creation and maintenance often entail significant temporal and financial investments. Embracing deep-learning-based approaches presents a promising avenue for revolutionizing aerial map generation, offering efficiencies hitherto unattainable. This research endeavors to harness the power of neural networks rooted in deep learning to craft a comprehensive land cover map. Focusing on Shiraz city, this study endeavors to delineate urban land uses into four distinct categories: Almond, Pistachio, Bare soil, and Shadow of trees. Leveraging imagery captured by a Phantom DJI 4 drone, the research scrutinizes ground features to facilitate accurate classification. The adoption of convolutional neural networks (CNN) emerges as a pivotal component of the methodology, serving as the bedrock for the automated classification process. Preliminary findings underscore the efficacy of the CNN approach, yielding an impressive overall accuracy rate of approximately 86.56%. Such results not only underscore the viability of deep-learning-based methodologies in land cover mapping but also underscore the potential for scalability and applicability across diverse urban landscapes. By mitigating the resource-intensive nature of traditional mapping techniques, this study paves the way for more agile and cost-effective urban planning endeavors, poised to accommodate the dynamic nature of modern cities.
[1] Z. Huang, M. Datcu, Z. Pan, and B. Lei, "A Hybrid and Explainable Deep Learning Framework for SAR Images," Int. Geosci. Remote Sens. Symp., pp. 1727–1730, Sep. 2020, doi: 10.1109/IGARSS39084.2020.9323845.
[2] B. Zheng, S. W. Myint, P. S. Thenkabail, and R. M. Aggarwal, "A support vector machine to identify irrigated crop types using time-series Landsat NDVI data," Int. J. Appl. Earth Obs. Geoinf., vol. 34, no. 1, pp. 103–112, 2015, doi: 10.1016/J.JAG.2014.07.002.
[3] N. Ammour, H. Alhichri, Y. Bazi, B. Benjdira, N. Alajlan, and M. Zuair, "Deep Learning Approach for Car Detection in UAV Imagery," Remote Sens. 2017, Vol. 9, Page 312, vol. 9, no. 4, p. 312, Mar. 2017, doi: 10.3390/RS9040312.
[4] C. Fan and R. Lu, "UAV image crop classification based on deep learning with spatial and spectral features," IOP Conf. Ser. Earth Environ. Sci., vol. 783, no. 1, p. 012080, May 2021, doi: 10.1088/1755-1315/783/1/012080.
[5] M. Der Yang, K. S. Huang, Y. H. Kuo, H. P. Tsai, and L. M. Lin, "Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery," Remote Sens. 2017, Vol. 9, Page 583, vol. 9, no. 6, p. 583, Jun. 2017, doi: 10.3390/RS9060583.
[6] M. Si, "Development of Predictive Emissions Monitoring System Using Open Source Machine Learning Library – Keras : A Case Study on a Cogeneration Unit," vol. 7, no. x, 2019, doi: 10.1109/ACCESS.2019.2930555.
[7] G. Fu, C. Liu, R. Zhou, T. Sun, and Q. Zhang, "Classification for high resolution remote sensing imagery using a fully convolutional network," Remote Sens., vol. 9, no. 5, May 2017, doi: 10.3390/rs9050498.
[8] N. Pourhasan, R. Shah-Hosseini, and S. T. Seydi, "Deep Learning-based Classification Method for Crop Mapping Using Time Series Satellite Images," J. Geomatics Sci. Technol., vol. 11, no. 1, pp. 129–142, 2021, Accessed: Oct. 14, 2022. [Online]. Available: http://jgst.issge.ir/article-1-938-en.html
[9] M. Ohsaki, P. Wang, K. Matsuda, S. Katagiri, H. Watanabe, and A. Ralescu, "Confusion-matrix-based kernel logistic regression for imbalanced data classification," IEEE Trans. Knowl. Data Eng., vol. 29, no. 9, pp. 1806–1819, 2017, doi: 10.1109/TKDE.2017.2682249.
[10] J. Wang, Y. Yang, and B. Xia, "A Simplified Cohen's Kappa for Use in Binary Classification Data Annotation Tasks," IEEE Access, vol. 7, pp. 164386–164397, 2019, doi: 10.1109/ACCESS.2019.2953104.
[11] Chasmer, L.; Hopkinson, C.; Veness, T.; Quinton, W.; Baltzer, J. A decision-tree classification for low-lying complex land cover types within the zone of discontinuous permafrost. Remote Sens. Environ. 2014, 143, 73–84.
[12] Benz, U.C.; Hofmann, P.; Willhauck, G.; Lingenfelder, I.; Heynen, M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 2004, 58, 239–258.
[13] Zhang, L.; Li, X.; Yuan, Q.; Liu, Y. Object-based approach to national land cover mapping using HJ satellite imagery. J. Appl. Remote Sens. 2014, 8, 083686.
