{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:38:02Z","timestamp":1773801482524,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T00:00:00Z","timestamp":1676937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41901282"],"award-info":[{"award-number":["41901282"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42101381"],"award-info":[{"award-number":["42101381"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971311"],"award-info":[{"award-number":["41971311"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2008085QD188"],"award-info":[{"award-number":["2008085QD188"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["201903a07020014"],"award-info":[{"award-number":["201903a07020014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202104b11020022"],"award-info":[{"award-number":["202104b11020022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of Anhui","award":["41901282"],"award-info":[{"award-number":["41901282"]}]},{"name":"National Natural Science Foundation of Anhui","award":["42101381"],"award-info":[{"award-number":["42101381"]}]},{"name":"National Natural Science Foundation of Anhui","award":["41971311"],"award-info":[{"award-number":["41971311"]}]},{"name":"National Natural Science Foundation of Anhui","award":["2008085QD188"],"award-info":[{"award-number":["2008085QD188"]}]},{"name":"National Natural Science Foundation of Anhui","award":["201903a07020014"],"award-info":[{"award-number":["201903a07020014"]}]},{"name":"National Natural Science Foundation of Anhui","award":["202104b11020022"],"award-info":[{"award-number":["202104b11020022"]}]},{"name":"Science and Technology Major Project of Anhui Province","award":["41901282"],"award-info":[{"award-number":["41901282"]}]},{"name":"Science and Technology Major Project of Anhui Province","award":["42101381"],"award-info":[{"award-number":["42101381"]}]},{"name":"Science and Technology Major Project of Anhui Province","award":["41971311"],"award-info":[{"award-number":["41971311"]}]},{"name":"Science and Technology Major Project of Anhui Province","award":["2008085QD188"],"award-info":[{"award-number":["2008085QD188"]}]},{"name":"Science and Technology Major Project of Anhui Province","award":["201903a07020014"],"award-info":[{"award-number":["201903a07020014"]}]},{"name":"Science and Technology Major Project of Anhui Province","award":["202104b11020022"],"award-info":[{"award-number":["202104b11020022"]}]},{"name":"International Science and Technology Cooperation Special","award":["41901282"],"award-info":[{"award-number":["41901282"]}]},{"name":"International Science and Technology Cooperation Special","award":["42101381"],"award-info":[{"award-number":["42101381"]}]},{"name":"International Science and Technology Cooperation Special","award":["41971311"],"award-info":[{"award-number":["41971311"]}]},{"name":"International Science and Technology Cooperation Special","award":["2008085QD188"],"award-info":[{"award-number":["2008085QD188"]}]},{"name":"International Science and Technology Cooperation Special","award":["201903a07020014"],"award-info":[{"award-number":["201903a07020014"]}]},{"name":"International Science and Technology Cooperation Special","award":["202104b11020022"],"award-info":[{"award-number":["202104b11020022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Individuals with abnormalities are key drivers of subtle stress changes in forest ecosystems. Although remote sensing monitoring and deep learning have been developed for forest ecosystems, they are faced with the complexity of forest landscapes, multiple sources of remote sensing data, high monitoring costs, and complex terrain, which pose significant challenges to automatic identification. Therefore, taking pine nematode disease as an example, this paper proposes D-SCNet, an intelligent monitoring network for abnormal individuals applicable to UAV visible images. In this method, the convolutional block attention model and simplified dense block are introduced to enhance the semantic analysis ability of abnormal individual identification, use multi-level information of abnormal individuals well, enhance feature transfer as well as feature weights between network layers, and selectively focus on abnormal features of individuals while reducing feature redundancy and parameter and improving monitoring accuracy and efficiency. This method uses lightweight deep learning models through weak information sources to achieve rapid monitoring of a large range of abnormal individuals in complex environments. With the advantages of low cost, high efficiency, and simple data sources, it is expected to further enhance the practicality and universality of intelligent monitoring of anomalous individuals by UAV remote sensing.<\/jats:p>","DOI":"10.3390\/rs15051181","type":"journal-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T01:39:47Z","timestamp":1677029987000},"page":"1181","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Recognition of Abnormal Individuals Based on Lightweight Deep Learning Using Aerial Images in Complex Forest Landscapes: A Case Study of Pine Wood Nematode"],"prefix":"10.3390","volume":"15","author":[{"given":"Zuyi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3594-7953","authenticated-orcid":false,"given":"Biao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Anhui Geographic Information Intelligent Technology Engineering Research Center, Hefei 230601, China"}]},{"given":"Wenwen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"}]},{"given":"Yanlan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Anhui Geographic Information Intelligent Technology Engineering Research Center, Hefei 230601, China"},{"name":"Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China"}]},{"given":"Jun","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"}]},{"given":"Peng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"}]},{"given":"Hanlu","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"}]},{"given":"Ao","family":"He","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.gloenvcha.2017.01.002","article-title":"Trees, forests and water: Cool insights for a hot world","volume":"43","author":"Ellison","year":"2017","journal-title":"Glob. Environ. Chang."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1111\/nph.15667","article-title":"Having the right neighbors: How tree species diversity modulates drought impacts on forests","volume":"228","author":"Grossiord","year":"2020","journal-title":"New Phytol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111383","DOI":"10.1016\/j.rse.2019.111383","article-title":"Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years","volume":"233","author":"Xiao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1126\/science.aad5068","article-title":"Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests","volume":"351","author":"Wu","year":"2016","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1146\/annurev-ecolsys-110512-135914","article-title":"The Structure, Distribution, and Biomass of the World's Forests","volume":"44","author":"Pan","year":"2013","journal-title":"Annu. Rev. Ecol. Evol. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.biocon.2018.04.008","article-title":"Combining global tree cover loss data with historical national forest cover maps to look at six decades of deforestation and forest fragmentation in Madagascar","volume":"222","author":"Vieilledent","year":"2018","journal-title":"Biol. Conserv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1093\/forestry\/cpw018","article-title":"Forest health in a changing world: Effects of globalization and climate change on forest insect and pathogen impacts","volume":"89","author":"Ramsfield","year":"2016","journal-title":"Forestry"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104966","DOI":"10.1016\/j.envint.2019.104966","article-title":"Economic losses due to ozone impacts on human health, forest productivity and crop yield across China","volume":"131","author":"Feng","year":"2019","journal-title":"Environ. Int."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s10340-018-1004-y","article-title":"Improved biosecurity surveillance of non-native forest insects: A review of current methods","volume":"92","author":"Poland","year":"2019","journal-title":"J. Pest Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.foreco.2017.04.011","article-title":"Generalized biomass and leaf area allometric equations for European tree species incorporating stand structure, tree age and climate","volume":"396","author":"Forrester","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1111\/nph.13477","article-title":"Tree mortality from drought, insects, and their interactions in a changing climate","volume":"208","author":"Anderegg","year":"2015","journal-title":"New Phytol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1508857.1508864","article-title":"Detecting outlying properties of exceptional objects","volume":"34","author":"Angiulli","year":"2009","journal-title":"ACM Trans. Database Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1146\/annurev-ento-041720-075234","article-title":"Tree Diversity and Forest Resistance to Insect Pests: Patterns, Mechanisms, and Prospects","volume":"66","author":"Jactel","year":"2021","journal-title":"Annu. Rev. Entomol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4515","DOI":"10.3390\/rs6054515","article-title":"Evaluating the Potential of WorldView-2 Data to Classify Tree Species and Different Levels of Ash Mortality","volume":"6","author":"Waser","year":"2014","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/rs3061211","article-title":"Use of Remote Sensing to Support Forest and Wetlands Policies in the USA","volume":"3","author":"Mayer","year":"2011","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1016\/j.tree.2015.08.009","article-title":"Biodiversity and Resilience of Ecosystem Functions","volume":"30","author":"Oliver","year":"2015","journal-title":"Trends Ecol. Evol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gaparovi, M., and Dobrini, D. (2020). Comparative assessment of machine learning methods for urban vegetation mapping using multitemporal Sentinel-1 imagery. Remote Sens., 12.","DOI":"10.3390\/rs12121952"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, W., Peng, W., Liu, X., He, G., and Cai, Y. (2022). Spatiotemporal Dynamics and Factors Driving the Distributions of Pine Wilt Disease-Damaged Forests in China. Forests, 13.","DOI":"10.3390\/f13020261"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guimar\u00e3es, N., P\u00e1dua, L., Marques, P., Silva, N., Peres, E., and Sousa, J.J. (2020). Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities. Remote Sens., 12.","DOI":"10.3390\/rs12061046"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, Z., Yang, R., Cai, W., Xue, Y., Hu, Y., and Li, L. (2022). LLAM-MDCNet for Detecting Remote Sensing Images of Dead Tree Clusters. Remote Sens., 14.","DOI":"10.3390\/rs14153684"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1038\/s41598-020-79653-9","article-title":"Explainable identification and mapping of trees using UAV RGB image and deep learning","volume":"11","author":"Onishi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2018.09.008","article-title":"UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras","volume":"146","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MGRS.2018.2867592","article-title":"Mini-UAV-Borne Hyperspectral Remote Sensing: From Observation and Processing to Applications","volume":"6","author":"Zhong","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1111\/j.1461-0248.2007.01073.x","article-title":"Tree diversity reduces herbivory by forest insects","volume":"10","author":"Jactel","year":"2007","journal-title":"Ecol. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Karmezi, M., Bataka, A., Papachristos, D., and Avtzis, D.N. (2022). Nematodes in the Pine Forests of Northern and Central Greece. Insects, 13.","DOI":"10.3390\/insects13020194"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.1111\/2041-210X.13912","article-title":"Existing and emerging uses of drones in restoration ecology","volume":"13","author":"Robinson","year":"2022","journal-title":"Methods Ecol. Evol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Olegario., T.V., Baldovino, R.G., and Bugtai, N.T. (2020, January 3\u20137). A Decision Tree-based Classification of Diseased Pine and Oak Trees Using Satellite Imagery. Proceedings of the 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Manila, Philippines.","DOI":"10.1109\/HNICEM51456.2020.9400002"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, S., Huang, H., Huang, Y., Cheng, D., and Huang, J. (2022). A GA and SVM Classification Model for Pine Wilt Disease Detection Using UAV-Based Hyperspectral Imagery. Appl. Sci., 12.","DOI":"10.3390\/app12136676"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1111\/2041-210X.13549","article-title":"Large-scale, image-based tree species mapping in a tropical forest using artificial perceptual learning","volume":"12","author":"Tang","year":"2021","journal-title":"Methods Ecol. Evol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2622","DOI":"10.1111\/2041-210X.13953","article-title":"Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation","volume":"13","author":"Ball","year":"2022","journal-title":"Methods Ecol. Evol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1640","DOI":"10.1111\/2041-210X.13901","article-title":"Deep learning as a tool for ecology and evolution","volume":"13","author":"Borowiec","year":"2022","journal-title":"Methods Ecol. Evol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"33679","DOI":"10.1109\/ACCESS.2020.2973658","article-title":"Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning","volume":"8","author":"Sun","year":"2020","journal-title":"IEEE Access"},{"key":"ref_33","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). European Conference on Computer Vision, Springer."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"898","DOI":"10.3389\/fpls.2020.00898","article-title":"Tomato Diseases and Pests Detection Based on Improved Yolo V3 Convolutional Neural Network","volume":"11","author":"Liu","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Stewart, E.L., Wiesner-Hanks, T., Kaczmar, N., DeChant, C., Wu, H., Lipson, H., Nelson, R.J., and Gore, M.A. (2019). Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning. Remote Sens., 11.","DOI":"10.3390\/rs11192209"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn[C]. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"106191","DOI":"10.1016\/j.compag.2021.106191","article-title":"A deep learning approach combining instance and semantic segmentation to identify diseases and pests of coffee leaves from in-field images","volume":"186","author":"Tassis","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, C., Jing, L., Li, H., and Tang, Y. (2021). A New Individual Tree Species Classification Method Based on the ResU-Net Model. Forests, 12.","DOI":"10.3390\/f12091202"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Qin, J., Wang, B., Wu, Y., Lu, Q., and Zhu, H. (2021). Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms. Remote Sens., 13.","DOI":"10.3390\/rs13020162"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bhujel, A., Kim, N.E., Arulmozhi, E., Basak, J.K., and Kim, H.T. (2022). A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification. Agriculture, 12.","DOI":"10.3390\/agriculture12020228"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.neucom.2021.03.035","article-title":"A survey: Deep learning for hyperspectral image classification with few labeled samples","volume":"448","author":"Jia","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.3390\/s20133729"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"119890","DOI":"10.1016\/j.foreco.2021.119890","article-title":"A multi-point aggregation trend of the outbreak of pine wilt disease in China over the past 20 years","volume":"505","author":"Hao","year":"2022","journal-title":"For. Ecol. Manag."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.3390\/ijms231810797"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/BF02348216","article-title":"Physiological Process of the Symptom Development and Resistance Mechanism in Pine Wilt Disease","volume":"2","author":"Fukuda","year":"1997","journal-title":"J. For. Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"109198","DOI":"10.1016\/j.ecolind.2022.109198","article-title":"Classification of pine wilt disease at different infection stages by diagnostic hyperspectral bands","volume":"142","author":"Li","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.aca.2009.11.045","article-title":"Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice","volume":"659","author":"Wu","year":"2010","journal-title":"Anal. Chim. Acta"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Choi, W.I., Song, H.J., Kim, D.S., Lee, D.S., Lee, C.Y., Nam, Y., Kim, J.B., and Park, Y.S. (2017). Dispersal Patterns of Pine Wilt Disease in the Early Stage of Its Invasion in South Korea. Forests, 8.","DOI":"10.3390\/f8110411"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"62561","DOI":"10.1109\/ACCESS.2020.2984130","article-title":"Automatic DenseNet Sparsification","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1109\/LGRS.2019.2930462","article-title":"Multi-Scale Spatial and Channel-wise Attention for Improving Object Detection in Remote Sensing Imagery","volume":"17","author":"Chen","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","first-page":"1","article-title":"Dense Convolutional Network and Its Application in Medical Image Analysis","volume":"2022","author":"Zhou","year":"2022","journal-title":"BioMed Res. Int."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1111\/2041-210X.13860","article-title":"Optimizing aerial imagery collection and processing parameters for drone-based individual tree mapping in structurally complex conifer forests","volume":"13","author":"Young","year":"2022","journal-title":"Methods Ecol. Evol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1111\/2041-210X.13120","article-title":"Machine learning to classify animal species in camera trap images: Applications in ecology","volume":"10","author":"Tabak","year":"2019","journal-title":"Methods Ecol. Evol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1181\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:38:26Z","timestamp":1760121506000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1181"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,21]]},"references-count":57,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051181"],"URL":"https:\/\/doi.org\/10.3390\/rs15051181","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,21]]}}}