Pine wood nematode disease area identification based on multi-temporal multi-source remote sensing images and BIT model

References

• Brockhaus J, Khorram S, Bruck R, Campbell M, Stallings C. 1992. A comparison of Landsat TM and SPOT HRV data for use in the development of forest defoliation models. Int J Remote Sens. 13(16):3235–3240. doi: 10.1080/01431169208904114.
   Web of Science ®Google Scholar
• Dai Z, Yang Z, Yang Y, Carbonell J, Le QV, Salakhutdinov R. 2019. Transformer-xl: attentive language models beyond a fixed-length context. In: 57th Annual Meeting of the Association-for-Computational-Linguistics (ACL); Florence, Italy.
   Google Scholar
• Devlin J, Chang M-W, Lee K, Toutanova K. 2018. Bert: pre-training of deep bidirectional transformers for language understanding. In: Conference of the North-American-Chapter of the Association-for-Computational-Linguistics - Human Language Technologies (NAACL-HLT); Minneapolis, MN.
   Google Scholar
• Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Houlsby N. 2020. An Image is Worth 16 × 16 Words: transformers for Image Recognition at Scale.
   Google Scholar
• Du H-Q, Ge H-L, Fan W-Y, Jin W, Zhou Y-F, Li JJS, Analysis S. 2009. Study on relationships between total chlorophyll with hyperspectral features for leaves of Pinus massoniana forest. Spectrosc Spect Anal. 29(11):3033–3037.
   Web of Science ®Google Scholar
• He J, Zhao L, Yang H, Zhang M, Li W, Sensing R. 2020. HSI-BERT: hyperspectral image classification using the bidirectional encoder representation from transformers. IEEE Trans Geosci Remote Sens. 58(1):165–178. doi: 10.1109/TGRS.2019.2934760.
   Web of Science ®Google Scholar
• Hellesen T, Matikainen L. 2013. An object-based approach for mapping shrub and tree cover on grassland habitats by use of LiDAR and CIR orthoimages. Remote Sens. 5(2):558–583. doi: 10.3390/rs5020558.
   Google Scholar
• Hu G, Yin C, Wan M, Zhang Y, Fang YJBE. 2020. Recognition of diseased Pinus trees in UAV images using deep learning and AdaBoost classifier. Biosyst Eng. 194:138–151. doi: 10.1016/j.biosystemseng.2020.03.021.
   Web of Science ®Google Scholar
• Huang L, Wang Y, Xu Q, Liu Q. 2021. Recognition of abnormally discolored trees caused by pine wilt disease using YOLO algorithm and UAV images. Trans Chin Soc Agric Eng. 37:197–203.
   Google Scholar
• Kim J-B, Jo M-H, Oh J-S, Lee K-J, Park S-J. 2001. Extraction method of damaged area by pine tree pest (Bursaphelenchus xylophilus) using remotely sensed data and GIS. In: Asian Conference on Remote Sensing 2001.
   Google Scholar
• Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks.
   Google Scholar
• Li Z, Chen G, Zhang T. 2020. A CNN-transformer hybrid approach for crop classification using multitemporal multisensor images. IEEE J Sel Top Appl Earth Observ Remote Sens. 13:847–858. doi: 10.1109/JSTARS.2020.2971763.
   Web of Science ®Google Scholar
• Liu S, Wang Q, Tang Q, Liu L, He H, Lu J, Dai X. 2022. High-resolution image identification of trees with pinewood nematode disease based on multi-feature extraction and deep learning of attention mechanism. J Eng. 7:177–184.
   Google Scholar
• Long J, Shelhamer E, Darrell T. 2015. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
   Google Scholar
• Luo Q, Xin W, Qiming X. 2017. Identification of pests and diseases of Dalbergia hainanensis based on EVI time series and classification of decision tree. In: IOP Conference Series: Earth and Environmental Science; IOP Publishing.
   Google Scholar
• Nakane K, Kimura YJER. 1992. Assessment of pine forest damage by blight based on Landsat TM data and correlation with environmental factors. Ecol Res. 7(1):9–18. doi: 10.1007/BF02348592.
   Web of Science ®Google Scholar
• Proença DN, Grass G, Morais PV. 2017. Understanding pine wilt disease: roles of the pine endophytic bacteria and of the bacteria carried by the disease-causing pinewood nematode. Microbiologyopen. 6(2):e415. doi: 10.1002/mbo3.415.
   Web of Science ®Google Scholar
• Qin J, Wang B, Wu Y, Lu Q, Zhu H. 2021. Identifying pine wood nematode disease using UAV images and deep learning algorithms. Remote Sens. 13(2):162. doi: 10.3390/rs13020162.
   Google Scholar
• Rançon F, Bombrun L, Keresztes B, Germain C. 2018. Comparison of SIFT encoded and deep learning features for the classification and detection of Esca disease in Bordeaux vineyards. Remote Sens. 11(1):1. doi: 10.3390/rs11010001.
   Google Scholar
• Ren D, Peng Y, Sun H, Yu M, Yu J, Liu Z. 2022. A global multi-scale channel adaptation network for pine wilt disease tree detection on UAV imagery by circle sampling. Drones. 6(11):353. doi: 10.3390/drones6110353.
   Google Scholar
• Shi Y, Skidmore AK, Wang T, Holzwarth S, Heiden U, Pinnel N, Zhu X, Heurich M. 2018. Tree species classification using plant functional traits from LiDAR and hyperspectral data. Int J Appl Earth Observ Geoinform. 73:207–219. doi: 10.1016/j.jag.2018.06.018.
   Web of Science ®Google Scholar
• Shuqing F, Wenmao C. 2003. Application of aerial video technique on the monitoring of pine disease from Bursapheleachus xylophilus. Chin Forest Sci Technol. 17(5):42–43.
   Google Scholar
• Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. 2017. Attention is all you need. In Advances in Neural Information Processing Systems 30.
   Google Scholar
• Wu H. 2016. A study of the potential of using worldview-2 of images for the detection of red attack pine tree. In: Eighth International Conference on Digital Image Processing (ICDIP 2016); SPIE.
   Google Scholar
• Wu B, Xu C, Dai X, Wan A, Zhang P, Yan Z, Tomizuka M, Gonzalez J, Keutzer K, Vajda P. 2020. Visual transformers: token-based image representation and processing for computer vision.
   Google Scholar
• Wu P, Yin Z, Yang H, Wu Y, Ma X. 2019. Reconstructing geostationary satellite land surface temperature imagery based on a multiscale feature connected convolutional neural network. Remote Sens. 11(3):300. doi: 10.3390/rs11030300.
   Google Scholar
• Xu D, Ren Y, Yan Z, Ye Q, DJJoFE Z. 2018. The CNN driven quality evaluation for UAV remote sensing images. J Forest Eng. 3(5):121–127.
   Google Scholar
• Yao X, Yang H, Wu Y, Wu P, Wang B, Zhou X, Wang S. 2019. Land use classification of the deep convolutional neural network method reducing the loss of spatial features. Sensors. 19(12):2792. doi: 10.3390/s19122792.
   PubMed Web of Science ®Google Scholar
• Yong-Xia LI, Xing-Yao Z. 2018. High risk of invasion and expansion of pine wood nematode in middle temperate zone of China. J Temp Forest Res. 1:3–6.
   Google Scholar
• Zhang Z, Wang B, Chen W, Wu Y, Qin J, Chen P, Sun H, He A. 2023. Recognition of abnormal individuals based on lightweight deep learning using aerial images in complex forest landscapes: a case study of pine wood nematode. Remote Sensing. 15(5):1181. doi: 10.3390/rs15051181.
   Web of Science ®Google Scholar
• Zhang R, Xia L, Chen L, Xie C, Chen M, Wang W. 2020. Recognition of wilt wood caused by pine wilt nematode based on U-Net network and unmanned aerial vehicle images. Trans Chin Soc Agricult Eng. 36(12):61–68.
   Google Scholar
• Zhang D, Zhang H, Tang J, Wang M, Hua X, Sun Q. 2020. Feature pyramid transformer. In: Computer Vision – ECCV 2020. 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVIII 16. Cham: Springer.
   Google Scholar
• Zheng S, Lu J, Zhao H, Zhu X, Luo Z, Wang Y, Fu Y, Feng J, Xiang T, Torr PHS, et al. 2021. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
   Google Scholar
• Zhou Z, Li R, Ben Z, He B, Shi Y, Dai W. 2022. Automatic identification of Bursaphelenchus xylophilus from remote sensing images using residual network. J For Eng. 7:185–191.
   Google Scholar
• Zhu X, Wang R, Shi W, Yu Q, Li X, Chen X. 2023. Automatic detection and classification of dead nematode-infested pine wood in stages based on YOLO v4 and GoogLeNet. Forests. 14(3):601. doi: 10.3390/f14030601.
   Web of Science ®Google Scholar