{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T06:10:44Z","timestamp":1790057444794,"version":"4.0.1"},"reference-count":88,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T00:00:00Z","timestamp":1629763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO2 emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constrained by persistent cloud-cover conditions in tropical regions. Synthetic aperture radar data represent an alternative to this technical constraint. This study aimed to evaluate the performance of three machine learning algorithms applied to multitemporal pairs of COSMO-SkyMed images to detect timber exploitation in a forest concession located in the Jamari National Forest, Rond\u00f4nia State, Brazilian Amazon. The studied algorithms included random forest (RF), AdaBoost (AB), and multilayer perceptron artificial neural network (MLP-ANN). The geographical coordinates (latitude and longitude) of logged trees and the LiDAR point clouds before and after selective logging were used as ground truths. The best results were obtained when the MLP-ANN was applied with 50 neurons in the hidden layer, using the ReLu activation function and SGD weight optimizer, presenting 88% accuracy both for the pair of images used for training (images acquired in June and October) of the network and in the generalization test, applied on a second dataset (images acquired in January and June). This study showed that X-band SAR images processed by applying machine learning techniques can be accurately used for detecting selective logging activities in the Brazilian Amazon.<\/jats:p>","DOI":"10.3390\/rs13173341","type":"journal-article","created":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T04:43:43Z","timestamp":1629780223000},"page":"3341","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0952-7055","authenticated-orcid":false,"given":"Tahisa Neitzel","family":"Kuck","sequence":"first","affiliation":[{"name":"Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance Division, Institute for Advanced Studies (IEAv), S\u00e3o Jos\u00e9 dos Campos 12228-001, Brazil"},{"name":"Geoscience Institute, Universidade de Bras\u00edlia (UnB), Bras\u00edlia 70910-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5760-556X","authenticated-orcid":false,"given":"Edson Eyji","family":"Sano","sequence":"additional","affiliation":[{"name":"Geoscience Institute, Universidade de Bras\u00edlia (UnB), Bras\u00edlia 70910-900, Brazil"},{"name":"Embrapa Cerrados, Planaltina 73310-970, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0247-8449","authenticated-orcid":false,"given":"Polyanna da Concei\u00e7\u00e3o","family":"Bispo","sequence":"additional","affiliation":[{"name":"Department of Geography, School of Environment, Education and Development, University of Manchester, Manchester M13 9PL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5226-0435","authenticated-orcid":false,"given":"Elcio Hideiti","family":"Shiguemori","sequence":"additional","affiliation":[{"name":"Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance Division, Institute for Advanced Studies (IEAv), S\u00e3o Jos\u00e9 dos Campos 12228-001, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0556-3470","authenticated-orcid":false,"given":"Paulo Fernando Ferreira","family":"Silva Filho","sequence":"additional","affiliation":[{"name":"Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance Division, Institute for Advanced Studies (IEAv), S\u00e3o Jos\u00e9 dos Campos 12228-001, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5323-6100","authenticated-orcid":false,"given":"Eraldo Aparecido Trondoli","family":"Matricardi","sequence":"additional","affiliation":[{"name":"Forestry Department, Universidade de Bras\u00edlia (UnB), Bras\u00edlia 70919-970, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,24]]},"reference":[{"key":"ref_1","unstructured":"IPCC (2018). Summary for Policymakers. Global Warming of 1.5 \u00b0C. Global Warming of 1.5 \u00b0C. An IPCC Special Report on the Impacts of Global Warming of 1.5 \u00b0C above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, IPCC."},{"key":"ref_2","unstructured":"SEEG-Brasil (2021, June 17). Sistema de Estimativa de Emiss\u00f5es de Gases de Efeito Estufa. Available online: http:\/\/plataforma.seeg.eco.br\/total_emission#."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1038\/s41558-021-01026-5","article-title":"Carbon Loss from Forest Degradation Exceeds that from Deforestation in the Brazilian Amazon","volume":"11","author":"Qin","year":"2021","journal-title":"Nat. Clim. Chang."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1126\/science.abb3021","article-title":"Long-Term Forest Degradation Surpasses Deforestation in the Brazilian Amazon","volume":"369","author":"Matricardi","year":"2020","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s00267-009-9337-1","article-title":"Balancing Conservation and Economic Sustainability: The Future of the Amazon Timber Industry","volume":"44","author":"Merry","year":"2009","journal-title":"Environ. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1080\/01431161.2012.717182","article-title":"Assessment of Forest Disturbances by Selective Logging and Forest Fires in the Brazilian Amazon Using Landsat Data","volume":"34","author":"Matricardi","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","unstructured":"Locks, C.J. (2017). Aplica\u00e7\u00f5es da Tecnologia LiDAR no Monitoramento da Explora\u00e7\u00e3o Madeireira em \u00c1reas de Concess\u00e3o Florestal. [Master\u2019s Thesis, Universidade de Bras\u00edlia]."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1126\/science.1118051","article-title":"Selective Logging in the Brazilian Amazon","volume":"305","author":"Asner","year":"2005","journal-title":"Science"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1590\/S0103-40142005000200005","article-title":"A Quest\u00e3o Fundi\u00e1ria na Amaz\u00f4nia","volume":"19","author":"Loureiro","year":"2005","journal-title":"Estud. Avan\u00e7ados"},{"key":"ref_10","unstructured":"INPE (2021, June 21). Monitoramento do Desmatamento da Floresta Amaz\u00f4nica Brasileira por Sat\u00e9lite. Available online: http:\/\/www.obt.inpe.br\/OBT\/assuntos\/programas\/amazonia\/prodes."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bem, P.P., Carvalho, O.A., Guimar\u00e3es, R.F., and Gomes, R.A.T. (2020). Change Detection of Deforestation in the Brazilian Amazon Using Landsat Data and Convolutional Neural Networks. Remote Sens., 12.","DOI":"10.3390\/rs12060901"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.apgeog.2018.10.003","article-title":"Deforestation Pattern Dynamics in Protected Areas of the Brazilian Legal Amazon Using Remote Sensing Data","volume":"100","author":"Cabral","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"012038","DOI":"10.1088\/1755-1315\/149\/1\/012038","article-title":"Measuring deforestation using remote sensing and its implication for conservation in Gunung Palung National Park, West Kalimantan, Indonesia","volume":"149","author":"Fawzi","year":"2018","journal-title":"IOP Conf. Ser. Earth  Environ. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2018.11.018","article-title":"Quantifying the Trade-off between Cost and Precision in Estimating Area of Forest Loss and Degradation Using Probability Sampling in Guyana","volume":"221","author":"Pickering","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3855","DOI":"10.1080\/01431160010006926","article-title":"Cloud Cover in Landsat Observations of the Brazilian Amazon","volume":"22","author":"Asner","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bouvet, A., Mermoz, S., Ball\u00e8re, M., Koleck, T., and Le Toan, T. (2018). Use of the SAR Shadowing Effect for Deforestation Detection with Sentinel-1 Time Series. Remote Sens., 10.","DOI":"10.3390\/rs10081250"},{"key":"ref_17","unstructured":"Fiorentino, C., and Virelli, M. (2021, June 16). COSMO-SkyMed Mission and Products Description, Available online: https:\/\/earth.esa.int\/eogateway\/documents\/20142\/37627\/COSMO-SkyMed-Mission-Products-Description.pdf."},{"key":"ref_18","unstructured":"Iceye (2021). Iceye Sar Product Guide, Iceye."},{"key":"ref_19","unstructured":"ESA (2021, July 06). TerraSAR-X\/TanDEM-X Full Archive and Tasking. Available online: https:\/\/earth.esa.int\/eogateway\/catalog\/terrasar-x-tandem-x-full-archive-and-tasking."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1080\/07038992.2014.913477","article-title":"Integration of Polarimetric PALSAR Attributes and Local Geomorphometric Variables Derived from SRTM for Forest Biomass Modeling in Central Amazonia","volume":"40","author":"Bispo","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bispo, P.C., Rodr\u00edguez-Veiga, P., Zimbres, B., Miranda, S.C., Cezare, C.H.G., Fleming, S., Baldacchino, F., Louis, V., Rains, D., and Garcia, M. (2020). Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach. Remote Sens., 12.","DOI":"10.3390\/rs12172685"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3927","DOI":"10.5194\/essd-13-3927-2021","article-title":"The Global Forest Above-Ground Biomass Pool for 2010 Estimated from High-Resolution Satellite Observations","volume":"13","author":"Santoro","year":"2021","journal-title":"Earth Syst. Sci. Data Discuss."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2014.11.016","article-title":"TanDEM-X Data for Aboveground Biomass Retrieval in a Tropical Peat Swamp Forest","volume":"158","author":"Schlund","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/LGRS.2014.2334140","article-title":"Tropical-Forest Biomass Estimation at X-Band from the Spaceborne Tandem-X Interferometer","volume":"12","author":"Treuhaft","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Treuhaft, R., Lei, Y., Gon\u00e7alves, F., Keller, M., Santos, J.R., Neumann, M., and Almeida, A. (2017). Tropical-Forest Structure and Biomass Dynamics from TanDEM-X Radar Interferometry. Forests, 8.","DOI":"10.3390\/f8080277"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"648","DOI":"10.3390\/rs5020648","article-title":"Mapping Tropical Rainforest Canopy Disturbances in 3D by COSMO-SkyMed Spotlight InSAR-Stereo Data to Detect Areas of Forest Degradation","volume":"5","author":"Deutscher","year":"2013","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.rse.2018.04.009","article-title":"Quantification of Selective Logging in Tropical Forest with Spaceborne SAR Interferometry","volume":"211","author":"Lei","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Berninger, A., Lohberger, S., St\u00e4ngel, M., and Siegert, F. (2018). SAR-Based Estimation of above-Ground Biomass and its Changes in Tropical Forests of Kalimantan Using L- and C-Band. Remote Sens., 10.","DOI":"10.3390\/rs10060831"},{"key":"ref_29","first-page":"102","article-title":"Potential of TerraSAR-X and Sentinel 1 Imagery to Map Deforested Areas and Derive Degradation Status in Complex Rain Forests of Ecuador","volume":"19","author":"Fassnacht","year":"2017","journal-title":"Int. For. Rev."},{"key":"ref_30","unstructured":"Macedo, C.R., and Ogashawara, I. (2013, January 13\u201318). Compara\u00e7\u00e3o de Filtros Adaptativos para Redu\u00e7\u00e3o do Ru\u00eddo Speckle em Imagens SAR. Proceedings of the XVI Simp\u00f3sio Bras. Sensoriamento Remoto, Foz do Igua\u00e7u, Brazil."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1109\/JSTARS.2015.2465167","article-title":"A New Image Quality Index for Objectively Evaluating Despeckling Filtering in SAR Images","volume":"9","author":"Gomez","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1080\/17538947.2017.1351582","article-title":"Speckle Filtering of Synthetic Aperture Radar Images Using Filters with Object-Size-Adapted Windows","volume":"11","author":"Mahdavi","year":"2018","journal-title":"Int. J. Digit. Earth"},{"key":"ref_33","first-page":"1","article-title":"Performance of Speckle Filters for COSMO-SkyMed Images from the Brazilian Amazon","volume":"99","author":"Kuck","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Shi, W., Zhang, M., Zhang, R., Chen, S., and Zhan, Z. (2020). Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges. Remote Sens., 12.","DOI":"10.3390\/rs12101688"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1109\/LGRS.2020.2994163","article-title":"SAR Images Change Detection Based on Self-Adaptive Network Architecture","volume":"18","author":"Shi","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, R., Wang, R., Huang, J., Li, J., and Jiao, L. (2020). Change Detection in SAR Images Using Multiobjective Optimization and Ensemble Strategy. IEEE Geosci. Remote Sens. Lett.","DOI":"10.1109\/LGRS.2020.3005140"},{"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","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.apgeog.2018.05.011","article-title":"Aboveground Biomass Estimation Using Multi-Sensor Data Synergy and Machine Learning Algorithms in a Dense Tropical Forest","volume":"96","author":"Ghosh","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Seo, D.K., Kim, Y.H., Eo, Y.D., Lee, M.H., and Park, W.Y. (2018). Fusion of SAR and Multispectral Images Using Random Forest Regression for Change Detection. ISPRS Int. J. Geo. Inf., 7.","DOI":"10.3390\/ijgi7100401"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1007\/s11676-017-0530-4","article-title":"Forest Type Identification by Random Forest Classification Combined with SPOT and Multitemporal SAR Data","volume":"29","author":"Yu","year":"2018","journal-title":"J. For. Res."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhao, X., Jiang, Y., and Stathaki, T. (2017). Automatic Target Recognition Strategy for Synthetic Aperture Radar Images Based on Combined Discrimination Trees. Comput. Intell. Neurosci., 7186120.","DOI":"10.1155\/2017\/7186120"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1008","DOI":"10.1109\/LGRS.2019.2939156","article-title":"SAR Target Small Sample Recognition Based on CNN Cascaded Features and AdaBoost Rotation Forest","volume":"17","author":"Zhang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Camargo, F.F., Sano, E.E., Almeida, C.M., Mura, J.C., and Almeida, T. (2019). A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2\/PALSAR-2 Polarimetric Images. Remote Sens., 11.","DOI":"10.3390\/rs11131600"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lee, Y.S., Lee, S., Baek, W.K., Jung, H.S., Park, S.H., and Lee, M.J. (2020). Mapping Forest Vertical Structure in Jeju Island from Optical and Radar Satellite Images Using Artificial Neural Network. Remote Sens., 12.","DOI":"10.3390\/rs12050797"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"15389","DOI":"10.1109\/ACCESS.2018.2889326","article-title":"Local Descriptor Learning for Change Detection in Synthetic Aperture Radar Images via Convolutional Neural Networks","volume":"7","author":"Dong","year":"2019","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5751","DOI":"10.1109\/TGRS.2019.2901945","article-title":"A Deep Learning Method for Change Detection in Synthetic Aperture Radar Images","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Cui, B., Zhang, Y., Yan, L., Wei, J., and Wu, H. (2019). An Unsupervised SAR Change Detection Method Based on Stochastic Subspace Ensemble Learning. Remote Sens., 11.","DOI":"10.3390\/rs11111314"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Jaturapitpornchai, R., Matsuoka, M., Kanemoto, N., Kuzuoka, S., Ito, R., and Nakamura, R. (2019). Newly Built Construction Detection in SAR Images Using Deep Learning. Remote Sens., 11.","DOI":"10.3390\/rs11121444"},{"key":"ref_49","unstructured":"IBGE (2012). Manual T\u00e9cnico da Vegeta\u00e7\u00e3o Brasileira, IBGE."},{"key":"ref_50","unstructured":"SFB (2021, June 16). Servi\u00e7o Florestal Brasileiro. Floresta Nacional do Jamari, Available online: http:\/\/www.florestal.gov.br\/florestas-sob-concessao\/92-concessoes-florestais\/florestas-sob-concessao\/101-floresta-nacional-do-jamari-ro."},{"key":"ref_51","unstructured":"Lopes, A., Nezry, E., Touzi, R., and Laur, H. (1990, January 20\u201324). Maximum a Posteriori Speckle Filtering and First Order Texture Models in SAR Images. Proceedings of the 10th Annual International Symposium on Geoscience and Remote Sensing, College Park, MD, USA."},{"key":"ref_52","unstructured":"Koeniguer, E., Nicolas, J.-M., and Janez, F. (2019, January 19\u201321). Worldwide Multitemporal Change Detection Using Sentinel-1 Images. Proceedings of the BIDS\u2014Conference on Big Data from Space, Munich, Germany."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Koeniguer, E.C., and Nicolas, J.M. (2020). Change Detection based on the Coefficient of Variation in SAR Time-Series of Urban Areas. Remote Sens., 12.","DOI":"10.3390\/rs12132089"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Deutscher, J., Gutjahr, K., Perko, R., Raggam, H., Hirschmugl, M., and Schardt, M. (2017, January 27\u201329). Humid Tropical Forest Monitoring with Multi-Temporal L-, C- and X-Band SAR Data. Proceedings of the 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Bruges, Belgium.","DOI":"10.1109\/Multi-Temp.2017.8035264"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2358","DOI":"10.1002\/hyp.6834","article-title":"Mapping Detention Basins and Deriving Their Spatial Attributes from Airborne LiDAR Data for Hydrological Applications","volume":"22","author":"Liu","year":"2008","journal-title":"Hydrol. Process."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Everitt, B.S., and Skrondal, A. (2010). The Cambridge Dictionary of Statistics, Cambridge University Press.","DOI":"10.1017\/CBO9780511779633"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1080\/014311698215775","article-title":"Cartographie des Zones Urbaines \u00e1 l\u2019aide Des Images A\u00e9roport\u00e9es MEIS-II","volume":"19","author":"Anys","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_59","first-page":"2349","article-title":"Orange: Data Mining Toolbox in Python","volume":"14","author":"Curk","year":"2013","journal-title":"J. Mach. Learn. Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s41664-018-0068-2","article-title":"On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning","volume":"2","author":"Xu","year":"2018","journal-title":"J. Anal. Test."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random Forest in Remote Sensing: A Review of Applications and Future Directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2015.03.002","article-title":"Random Forest and Rotation Forest for Fully Polarized SAR Image Classification Using Polarimetric and Spatial Features","volume":"105","author":"Du","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.isprsjprs.2012.01.005","article-title":"Oil Spill Feature Selection and Classification Using Decision Tree Forest on SAR Image Data","volume":"68","author":"Topouzelis","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"e00938","DOI":"10.1016\/j.heliyon.2018.e00938","article-title":"State-of-the-Art in Artificial Neural Network Applications: A Survey","volume":"4","author":"Abiodun","year":"2018","journal-title":"Heliyon"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/JSTARS.2020.3005403","article-title":"Remote Sensing Image Scene Classification meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities","volume":"13","author":"Cheng","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_67","unstructured":"Haykin, S. (2008). Neural Networks and Learning Machines, Pearson Prentice Hall."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1080\/014311697218700","article-title":"Introduction Neural Networks in Remote Sensing","volume":"18","author":"Atkinson","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1561\/2000000039","article-title":"Deep Learning: Methods and Applications","volume":"7","author":"Deng","year":"2014","journal-title":"Found. Trends Signal Process."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1137\/0916069","article-title":"A Limited Memory Algorithm for Bound Constrained Optimization","volume":"16","author":"Byrd","year":"1995","journal-title":"SIAM J. Sci. Comput."},{"key":"ref_71","unstructured":"Bottou, L. (2010, January 22\u201327). Large-Scale Machine Learning with Stochastic Gradient Descent. Proceedings of the COMPSTAT the 19th International Conference on Computational Statistics, Keynote, Invited and Contributed Papers, Paris, France."},{"key":"ref_72","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_73","unstructured":"Powers, D.M.W. (2020). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. arXiv."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s10994-015-5486-z","article-title":"A Bayesian Approach for Comparing Cross-Validated Algorithms on Multiple Data Sets","volume":"100","author":"Corani","year":"2015","journal-title":"Mach. Learn."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1109\/JSTARS.2014.2313572","article-title":"Comparative Assessment of Supervised Classifiers for Land Use-Land Cover Classification in a Tropical Region Using Time-Series PALSAR Mosaic Data","volume":"7","author":"Shiraishi","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_76","first-page":"100112","article-title":"Hyper-Sinh: An Accurate and Reliable Function from Shallow to Deep Learning in TensorFlow and Keras","volume":"6","author":"Parisi","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1080\/01431161.2019.1694725","article-title":"Analysis of Various Optimizers on Deep Convolutional Neural Network Model in the Application of Hyperspectral Remote Sensing Image Classification","volume":"41","author":"Bera","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_78","unstructured":"Saueressig, D. (2018). Manual de Dendrologia: O Estudo Das \u00c1rvores, Plantas do Brasil Ltda. [1st ed.]."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1109\/TGRS.2009.2034843","article-title":"Evidence of Rainfall Signatures on X-Band Synthetic Aperture Radar Imagery over Land","volume":"48","author":"Marzano","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_80","unstructured":"SFB (2021, June 21). Servi\u00e7o Florestal Brasileiro. DETEX, Available online: https:\/\/www.florestal.gov.br\/monitoramento."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"e20170634","DOI":"10.1590\/2179-8087.063417","article-title":"Selective Logging Detection in the Brazilian Amazon","volume":"26","author":"Matricardi","year":"2019","journal-title":"Floresta Ambient."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2956","DOI":"10.1111\/gcb.15029","article-title":"Satellite-Based Estimates Reveal Widespread Forest Degradation in the Amazon","volume":"26","author":"Bullock","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Doblas, J., Shimabukuro, Y., Sant\u2019anna, S., Carneiro, A., Arag\u00e3o, L., and Almeida, C. (2020). Optimizing near Real-Time Detection of Deforestation on Tropical Rainforests Using Sentinel-1 Data. Remote Sens., 12.","DOI":"10.3390\/rs12233922"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","article-title":"Review on Convolutional Neural Networks (CNN) in Vegetation Remote Sensing","volume":"173","author":"Kattenborn","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_86","first-page":"2302","article-title":"Never-Ending Learning","volume":"3","author":"Mitchell","year":"2015","journal-title":"Proc. Natl. Conf. Artif. Intell."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"105343","DOI":"10.1016\/j.knosys.2019.105343","article-title":"Autoencoder Based Sample Selection for Self-Taught Learning","volume":"192","author":"Feng","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref_88","unstructured":"Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison Wesley."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3341\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:50:15Z","timestamp":1760165415000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3341"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,24]]},"references-count":88,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13173341"],"URL":"https:\/\/doi.org\/10.3390\/rs13173341","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,24]]}}}