Detection Method of Infected Wood on Digital Orthophoto Map–Digital Surface Model Fusion Network
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Published:2023-08-31
Issue:17
Volume:15
Page:4295
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ISSN:2072-4292
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Container-title:Remote Sensing
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language:en
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Short-container-title:Remote Sensing
Author:
Wang Guangbiao12ORCID, Zhao Hongbo1ORCID, Chang Qing1, Lyu Shuchang1ORCID, Liu Binghao1ORCID, Wang Chunlei1ORCID, Feng Wenquan1
Affiliation:
1. Department of Electronics and Information Engineering, Beihang University, Beijing 100191, China 2. Qingdao Research Institute of Beihang University, Qingdao 266000, China
Abstract
Pine wilt disease (PWD) is a worldwide affliction that poses a significant menace to forest ecosystems. The swift and precise identification of pine trees under infection holds paramount significance in the proficient administration of this ailment. The progression of remote sensing and deep learning methodologies has propelled the utilization of target detection and recognition techniques reliant on remote sensing imagery, emerging as the prevailing strategy for pinpointing affected trees. Although the existing object detection algorithms have achieved remarkable success, virtually all methods solely rely on a Digital Orthophoto Map (DOM), which is not suitable for diseased trees detection, leading to a large false detection rate in the detection of easily confused targets, such as bare land, houses, brown herbs and so on. In order to improve the ability of detecting diseased trees and preventing the spread of the epidemic, we construct a large-scale PWD detection dataset with both DOM and Digital Surface Model (DSM) images and propose a novel detection framework, DDNet, which makes full use of the spectral features and geomorphological spatial features of remote sensing targets. The experimental results show that the proposed joint network achieves an AP50 2.4% higher than the traditional deep learning network.
Funder
National Natural Science Foundation of China
Subject
General Earth and Planetary Sciences
Reference63 articles.
1. Pine wood nematode, Bursaphelenchus xylophilus;Futai;Annu. Rev. Phytopathol.,2013 2. Li, M., Li, H., Ding, X., Wang, L., Wang, X., and Chen, F. (2022). The detection of pine wilt disease: A literature review. Int. J. Mol. Sci., 23. 3. Wu, W., Zhang, Z., Zheng, L., Han, C., Wang, X., Xu, J., and Wang, X. (2020). Research progress on the early monitoring of pine wilt disease using hyperspectral techniques. Sensors, 20. 4. ImageNet Classification with Deep Convolutional Neural Networks;Krizhevsky;Adv. Neural Inf. Process. Syst.,2012 5. Girshick, R.B., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.
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