{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:34:48Z","timestamp":1760524488135,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T00:00:00Z","timestamp":1682553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["CBAS2022IRP03","41972308","20212AEI91006","2019QZKK0806"],"award-info":[{"award-number":["CBAS2022IRP03","41972308","20212AEI91006","2019QZKK0806"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["CBAS2022IRP03","41972308","20212AEI91006","2019QZKK0806"],"award-info":[{"award-number":["CBAS2022IRP03","41972308","20212AEI91006","2019QZKK0806"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangxi Provincial Technology Innovation Guidance Program (National Science and Technology Award Reserve Project Cultivation Program)","award":["CBAS2022IRP03","41972308","20212AEI91006","2019QZKK0806"],"award-info":[{"award-number":["CBAS2022IRP03","41972308","20212AEI91006","2019QZKK0806"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research","award":["CBAS2022IRP03","41972308","20212AEI91006","2019QZKK0806"],"award-info":[{"award-number":["CBAS2022IRP03","41972308","20212AEI91006","2019QZKK0806"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate identification of individual tree species (ITS) is crucial to forest management. However, current ITS identification methods are mainly based on traditional image features or deep learning. Traditional image features are more interpretative, but the generalization and robustness of such methods are inferior. In contrast, deep learning based approaches are more generalizable, but the extracted features are not interpreted; moreover, the methods can hardly be applied to limited sample sets. In this study, to further improve ITS identification, typical spectral and texture image features were weighted to assist deep learning models for ITS identification. To validate the hybrid models, two experiments were conducted; one on the dense forests of the Huangshan Mountains, Anhui Province and one on the Gaofeng forest farm, Guangxi Province, China. The experimental results demonstrated that with the addition of image features, different deep learning ITS identification models, such as DenseNet, AlexNet, U-Net, and LeNet, with different limited sample sizes (480, 420, 360), were all enhanced in both study areas. For example, the accuracy of DenseNet model with a sample size of 480 were improved to 87.67% from 85.41% in Huangshan. This hybrid model can effectively improve ITS identification accuracy, especially for UAV aerial imagery or limited sample sets, providing the possibility to classify ITS accurately in sample-poor areas.<\/jats:p>","DOI":"10.3390\/rs15092301","type":"journal-article","created":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T05:10:14Z","timestamp":1682572214000},"page":"2301","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Individual Tree Species Identification Based on a Combination of Deep Learning and Traditional Features"],"prefix":"10.3390","volume":"15","author":[{"given":"Caiyan","family":"Chen","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linhai","family":"Jing","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3565-1773","authenticated-orcid":false,"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2257-8749","authenticated-orcid":false,"given":"Yunwei","family":"Tang","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1144-0004","authenticated-orcid":false,"given":"Fulong","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education & School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1126\/science.263.5144.185","article-title":"Carbon Pools and Flux of Global Forest Ecosystems","volume":"263","author":"Dixon","year":"1994","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.ecocom.2010.02.005","article-title":"Tree species diversity and community com-position in a human-dominated tropical forest of Western Ghats biodiversity hotspot","volume":"7","author":"Anitha","year":"2010","journal-title":"India. Ecol. Complex."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4521","DOI":"10.1080\/01431161.2016.1214302","article-title":"How to assess the accuracy of the individual tree-based forest inventory derived from remotely sensed data: A review","volume":"37","author":"Yin","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","first-page":"65","article-title":"Multi-phenology WorldView-2 imagery improves remote sensing of savannah tree species","volume":"58","author":"Madonsela","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.foreco.2015.08.027","article-title":"Remote sensing proxies of productivity and moisture predict forest stand type and recovery rate following experimental harvest","volume":"357","author":"Nijland","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.isprsjprs.2017.11.018","article-title":"Predicting temperate for-est stand types using only structural profiles from discrete return airborne lidar","volume":"136","author":"Fedrigo","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Grabska, E., Hostert, P., Pflugmacher, D., and Ostapowicz, K. (2019). Forest Stand Species Mapping Using the Sentinel-2 Time Series. Remote Sens., 11.","DOI":"10.3390\/rs11101197"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Oreti, L., Giuliarelli, D., Tomao, A., and Barbati, A. (2021). Object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13132508"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wan, H.M., Tang, Y.W., Jing, L.H., Li, H., Qiu, F., and Wu, W.J. (2021). Tree species classification of forest stands using multi-source remote sensing data. Remote Sens., 13.","DOI":"10.3390\/rs13010144"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/S0034-4257(03)00140-8","article-title":"Identifying species of individual trees using airborne laser scanner","volume":"90","author":"Holmgren","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2012.03.013","article-title":"Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral\/hyperspectral images and LiDAR data","volume":"123","author":"Dalponte","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.3390\/rs4092661","article-title":"Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data","volume":"4","author":"Immitzer","year":"2012","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1080\/07038992.1995.10874622","article-title":"A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images","volume":"21","author":"Gougeon","year":"1995","journal-title":"Can. J. Remote. Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/S0098-3004(00)00110-2","article-title":"TIDA: An algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery","volume":"28","author":"Culvenor","year":"2002","journal-title":"Comput. Geosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.14358\/PERS.78.11.1275","article-title":"Automated delineation of individual tree crowns from Lidar data by multi-scale analysis and segmentation","volume":"78","author":"Jing","year":"2012","journal-title":"Photogramm. Eng. Remote Sen."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.isprsjprs.2017.07.001","article-title":"Vertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds","volume":"130","author":"Hamraz","year":"2017","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Qiu, L., Jing, L.H., Hu, B.X., Li, H., and Tang, Y.W. (2020). A new individual tree crown delineation method for high resolution multispectral imagery. Remote Sens., 12.","DOI":"10.3390\/rs12030585"},{"key":"ref_18","first-page":"349","article-title":"Assessing the utility WorldView-2 imagery for tree species mapping in South African subtropical humid forest and the conservation implications: Dukuduku forest patch as case study","volume":"38","author":"Cho","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2554","DOI":"10.1109\/JSTARS.2016.2569408","article-title":"Individual tree species classification from airborne multisensor imagery using robust PCA","volume":"9","author":"Lee","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","first-page":"1","article-title":"Classification of tree species composition using a combination of multispectral imagery and airborne laser scanning data","volume":"63","author":"Sedliak","year":"2017","journal-title":"For. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"43","DOI":"10.5194\/isprs-archives-XLII-4-W4-43-2017","article-title":"Woodland mapping at single-tree levels using object-oriented classification of unmanned aerial vehicle (UAV) images","volume":"42\u201344","author":"Chenari","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"126958","DOI":"10.1016\/j.ufug.2020.126958","article-title":"Urban forest monitoring based on multiple features at the single tree scale by UAV","volume":"58","author":"Wang","year":"2021","journal-title":"Urban. For. Urban. Green."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Maschler, J., Atzberger, C., and Immitzer, M. (2018). Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data. Remote Sens., 10.","DOI":"10.3390\/rs10081218"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mishra, N.B., Mainali, K.P., Shrestha, B.B., Radenz, J., and Karki, D. (2018). Species-Level Vegetation Mapping in a Himalayan Treeline Ecotone Using Unmanned Aerial System (UAS) Imagery. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7110445"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1080\/15481603.2020.1712102","article-title":"Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data","volume":"57","author":"Sothe","year":"2020","journal-title":"GIScience Remote Sens."},{"key":"ref_27","first-page":"102173","article-title":"Tree species classification using UAS-based digital aerial photogrammetry point clouds and multispectral imageries in subtropical natural forests","volume":"92","author":"Xu","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K.M., Zhang, X.Y., Ren, S.Q., and Sun, J. (2016, January 30). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fujimoto, A., Haga, C., Matsui, T., Machimura, T., Hayashi, K., Sugita, S., and Takagi, H. (2019). An end to end process development for UAV-SfM based forest monitoring: Individual tree detection, species classification and carbon dynamics simulation. Forests, 10.","DOI":"10.3390\/f10080680"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Egli, S., and H\u00f6pke, M. (2020). CNN-Based Tree Species Classification Using High Resolution RGB Image Data from Automated UAV Observations. Remote Sens., 12.","DOI":"10.3390\/rs12233892"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cao, K., and Zhang, X. (2020). An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images. Remote Sens., 12.","DOI":"10.3390\/rs12071128"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2021.01.024","article-title":"A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery","volume":"174","author":"Osco","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101207","DOI":"10.1016\/j.ecoinf.2020.101207","article-title":"Individual tree crown delineation from high-resolution UAV images in broadleaf forest","volume":"61","author":"Miraki","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Nezami, S., Khoramshahi, E., Nevalainen, O., Polonen, I., and Honkavaara, E. (2020). Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks. Remote Sens., 12.","DOI":"10.20944\/preprints202002.0334.v1"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yan, S., Jing, L., and Wang, H. (2021). A New Individual Tree Species Recognition Method Based on a Convolutional Neural Network and High-Spatial Resolution Remote Sensing Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13030479"},{"key":"ref_38","first-page":"349","article-title":"Classification of individual tree species in high-resolution remote sensing imagery based on convolution neural network","volume":"58","author":"Ouyang","year":"2021","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhou, J.W., Wang, H.W., Tan, T.Y., Cui, M.C., Huang, Z.L., Wang, P., and Zhang, L. (2022). Multi-species individual tree segmentation and identification based on improved mask R-CNN and UAV imagery in mixed forests. Remote Sens., 14.","DOI":"10.3390\/rs14040874"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.isprsjprs.2007.08.007","article-title":"Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site","volume":"63","author":"Mallinis","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.14358\/PERS.78.10.1079","article-title":"Mapping Individual tree species in an urban forest using airborne Lidar data and hyperspectral imagery","volume":"78","author":"Zhang","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.rse.2013.07.016","article-title":"Evaluation of simulated bands in air-borne optical sensors for tree species identification","volume":"138","author":"Pant","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.isprsjprs.2012.03.005","article-title":"Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment","volume":"69","author":"Naidoo","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.rse.2013.09.006","article-title":"Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data","volume":"140","author":"Dalponte","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of studies on tree species classification from remotely sensed data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"104252","DOI":"10.1016\/j.compbiomed.2021.104252","article-title":"Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph","volume":"131","author":"Jin","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"20821","DOI":"10.1007\/s11042-021-10612-w","article-title":"Bag-of-Visual-Words codebook generation using deep features for effective classification of imbalanced multi-class image datasets","volume":"80","author":"Saini","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"11499","DOI":"10.1007\/s00521-021-05816-y","article-title":"DeepVisDroid: Android malware detection by hybridizing image-based features with deep learning techniques","volume":"33","author":"Bakour","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.patrec.2020.12.010","article-title":"Customized VGG19 Architecture for Pneumonia Detection in Chest X-Rays","volume":"143","author":"Dey","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Varin, M., Chalghaf, B., and Joanisse, G. (2020). Object-Based Approach Using Very High Spatial Resolution 16-Band WorldView-3 and LiDAR Data for Tree Species Classification in a Broadleaf Forest in Quebec, Canada. Remote Sens., 12.","DOI":"10.3390\/rs12183092"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.1080\/01431160802549260","article-title":"Two improvement schemes of PAN modulation fusion methods for spectral distortion minimization","volume":"30","author":"Jing","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","unstructured":"Jing, L., Hu, B., Li, J., Noland, T., and Guo, H. (2013, January 22\u201326). Automated tree crown delineation from imagery based on morphological techniques. Proceedings of the 35th International Symposium on Remote Sensing of Environment (ISRSE35), Beijing, China."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.neunet.2020.01.018","article-title":"Theory of deep convolutional neural networks: Downsampling","volume":"124","author":"Zhou","year":"2020","journal-title":"Neural Netw."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Al-Azzawi, A., Ouadou, A., Max, H., Duan, Y., Tanner, J.J., and Cheng, J. (2020). DeepCryoPicker: Fully automated deep neural network for single protein particle picking in cryo-EM. BMC Bioinform., 21.","DOI":"10.1186\/s12859-020-03809-7"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"324","DOI":"10.7717\/peerj-cs.324","article-title":"A hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss","volume":"6","author":"Wang","year":"2020","journal-title":"PeerJ Comput. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"107610","DOI":"10.1016\/j.patcog.2020.107610","article-title":"Efficient densely connected convolutional neural networks","volume":"109","author":"Li","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/PROC.1979.11328","article-title":"Statistical and structural approaches to texture","volume":"67","author":"Haralick","year":"1979","journal-title":"Proc. IEEE"},{"key":"ref_61","first-page":"1449","article-title":"For Change Detection Using Various Accuracy","volume":"54","author":"Fung","year":"1988","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1080\/17538947.2023.2181992","article-title":"Reliability and consistency assessment of land cover products atmacro and local scales in typical cities","volume":"16","author":"Shi","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015). Medical Image Computing and Computer-Assisted Intervention, Springer International Publishing.","DOI":"10.1007\/978-3-319-24553-9"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_66","first-page":"114","article-title":"Picea abies in Europe: Distribution, habitat, usage and threats","volume":"1","author":"Caudullo","year":"2016","journal-title":"Eur. Atlas For. Tree Species"},{"key":"ref_67","first-page":"101960","article-title":"Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data","volume":"84","author":"Modzelewska","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/S0034-4257(01)00209-7","article-title":"Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method","volume":"77","author":"Ek","year":"2001","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2301\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:24:24Z","timestamp":1760124264000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2301"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,27]]},"references-count":69,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092301"],"URL":"https:\/\/doi.org\/10.3390\/rs15092301","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,4,27]]}}}