Categories
How to Cite
Abstract
Deep learning classification of invasive species using widely-used ArcGIS Pro software and increasingly common drone imagery can aid in identification and management of natural areas. A step-by-step implementation, with associated data for users to access, is presented to make this technology more widely accessible to GIS analysts, researchers, and graduate students working with remotely sensed data in the natural resource field.
References
Object Detection in Remote Sensing Images.” Neural Processing Letters 49: 1369–79. https://doi.org/https://doi.org/10.1007/s11063-018-9878-5.
Environmental Systems Research Institute. 2019. “ArcGIS Pro.”
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. Cambridge: MIT press.
Jensen, John R. 2016. Introductory Digital Image Processing: A Remote Sensing Perspective. 4th ed. Pearson.
Kakarla, Sri, and Yiannis Ampatzidis. 2019. “Postflight Data Processing Instructions on the Use of Unmanned Aerial Vehicles (UAVs) for Agricultural Applications.” EDIS 6 (6). https://doi.org/https://doi.org/10.32473/edis-ae533-2019.
Liu, T., and A. Abd-Elrahman. 2018. “An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System.” Remote Sensing 10 (3): 457.
Liu, Tao, and Amr Abd-Elrahman. 2018. “Deep Convolutional Neural Network Training Enrichment Using Multi-View Object-Based Analysis of Unmanned Aerial Systems Imagery for Wetlands Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 139 (May): 154–70. https://doi.org/10.1016/j.isprsjprs.2018.03.006.
Liu, Tao, Amr Abd-Elrahman, Bon Dewitt, Scot Smith, Jon Morton, and Victor L. Wilhelm. 2019. “Evaluating the Potential of Multi-View Data Extraction from Small Unmanned Aerial Systems (UASs) for Object-Based Classification for Wetland Land Covers.” GIScience & Remote Sensing 56 (1): 130–59. https://www.tandfonline.com/doi/full/10.1080/15481603.2018.1495395.
Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” In International Conference on Medical Image Computing and Computer-Assisted Intervention, edited by N. Navab, J. Hornegger, W. Wells, and A. Frangi, 234–41. Springer, Cham. https://doi.org/https://doi.org/10.1007/978-3-319-24574-4_28.
Watts, A. C., W .S. Bowman, A. Abd-Elrahman, A. Mohamed, B. Wilkinson, J. Perry, Y. Kaddoura, and K. Lee. 2008. “Unmanned Aircraft Systems (UASs) for Ecological Research and Natural-Resource Monitoring (Florida).” Ecological Restoration 26 (1): 13–14. http://er.uwpress.org/content/26/1/13.short.
Zhao, Z., P. Zheng, S. Xu, and Wu. Xi. 2019. “Object Detection with Deep Learning: A Review.” IEEE Transactions on Neural Networks and Learning Systems 30 (11): 3212–32. https://doi.org/10.1109/TNNLS.2018.2876865.