Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/87901
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.identifier.isbnpt_BR
dc.contributor.authorunicampFalcão, Alexandre Xavierpt_BR
dc.typeArtigopt_BR
dc.titleToward satellite-based land cover classification through optimum-path forestpt_BR
dc.title.alternativept_BR
dc.contributor.authorPisani, Rodrigo Josépt_BR
dc.contributor.authorNakamura, Rodrigo Yuji Mizobept_BR
dc.contributor.authorRiedel, Paulina Settipt_BR
dc.contributor.authorZimback, Célia Regina Lopespt_BR
dc.contributor.authorFalcão, Alexandre Xavierpt_BR
dc.contributor.authorPapa, João Paulopt_BR
unicamp.authorFalcao, A.X., Institute of Computing, Unicamp-University of Campinas, 13083-859 Campinas, Brazilpt_BR
unicamp.author.externalPisani, R.J., Institute of Geoscience and Exact Sciences, Unesp-Universidade Estadual Paulista, 13506-900 Rio-Claro, Brazilpt
unicamp.author.externalNakamura, R.Y.M., Department of Computer Science, Unesp-Universidade Estadual Paulista, 17040 Bauru, Brazilpt
unicamp.author.externalRiedel, P.S., Institute of Geoscience and Exact Sciences, Unesp-Universidade Estadual Paulista, 13506-900 Rio-Claro, Brazilpt
unicamp.author.externalZimback, C.R.L., School of Agronomic Sciences, Unesp-Universidade Estadual Paulista, 18618-970 Botucatu, Brazilpt
unicamp.author.externalPapa, J.P., Department of Computer Science, Unesp-Universidade Estadual Paulista, 17040 Bauru, Brazilpt
dc.subjectReconhecimento de padrõespt_BR
dc.subjectFloresta de caminhos ótimospt_BR
dc.subjectSensoriamento remotopt_BR
dc.subjectImagens de sensoriamento remotopt_BR
dc.subject.otherlanguagePattern recognitionpt_BR
dc.subject.otherlanguageOptimum-path forestpt_BR
dc.subject.otherlanguageRemote sensingpt_BR
dc.subject.otherlanguageRemote-sensing imagespt_BR
dc.description.abstractLand cover classification has been paramount in the last years. Since the amount of information acquired by satellite on-board imaging systems has increased, there is a need for automatic tools that can tackle such problem. Despite the fact that one can find several works in the literature, we propose a novel methodology for land cover classification by means of the optimum-path forest (OPF) framework, which has never been applied to this context up to date. Experiments were conducted in supervised and unsupervised situations against some state-of-the-art pattern recognition techniques, such as support vector machines, Bayesian classifier, k-means, and mean shift. We had shown that supervised OPF can outperform such approaches, being much faster than all. In regard to clustering techniques, all classifiers have achieved similar results. © 1980-2012 IEEE.en
dc.description.abstractLand cover classification has been paramount in the last years. Since the amount of information acquired by satellite on-board imaging systems has increased, there is a need for automatic tools that can tackle such problem. Despite the fact that one can fpt_BR
dc.relation.ispartofIEEE transactions on geoscience and remote sensingpt_BR
dc.relation.ispartofabbreviationIEEE trans. geosci. remote sens.pt_BR
dc.publisher.cityPiscataway, NJpt_BR
dc.publisher.countryEstados Unidospt_BR
dc.publisherInstitute of Electrical and Electronics Engineerspt_BR
dc.date.issued2014pt_BR
dc.date.monthofcirculationOct.pt_BR
dc.identifier.citationIeee Transactions On Geoscience And Remote Sensing. Institute Of Electrical And Electronics Engineers Inc., v. 52, n. 10, p. 6075 - 6085, 2014.pt_BR
dc.language.isoengpt_BR
dc.description.volume52pt_BR
dc.description.issuenumber10pt_BR
dc.description.issuesupplementpt_BR
dc.description.issuepartpt_BR
dc.description.issuespecialpt_BR
dc.description.firstpage6075pt_BR
dc.description.lastpage6085pt_BR
dc.rightsfechadopt_BR
dc.rightsFechadopt_br
dc.sourceSCOPUSpt_BR
dc.identifier.issn0196-2892pt_BR
dc.identifier.eissn1558-0644pt_BR
dc.identifier.doi10.1109/TGRS.2013.2294762pt_BR
dc.identifier.urlhttps://ieeexplore.ieee.org/document/6719506pt_BR
dc.description.sponsorshipFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOpt_BR
dc.description.sponsorshipCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOpt_BR
dc.description.sponsorshipFUNDUNESP - FUNDAÇÃO PARA O DESENVOLVIMENTO DA UNIVERSIDADE ESTADUAL PAULISTApt_BR
dc.description.sponsordocumentnumber2009/16206-1; 2010/11676-7pt_BR
dc.description.sponsordocumentnumber303182/2011-3; 303673/2010-9pt_BR
dc.description.sponsordocumentnumbersem informaçãopt_BR
dc.date.available2015-06-25T18:02:46Z
dc.date.available2015-11-26T15:04:55Z-
dc.date.accessioned2015-06-25T18:02:46Z
dc.date.accessioned2015-11-26T15:04:55Z-
dc.description.provenanceMade available in DSpace on 2015-06-25T18:02:46Z (GMT). No. of bitstreams: 1 2-s2.0-84902077626.pdf: 3610408 bytes, checksum: 85f65e26c9aba0540a28f80acce668d1 (MD5) Previous issue date: 2014 Bitstreams deleted on 2021-01-04T14:26:02Z: 2-s2.0-84902077626.pdf,. Added 1 bitstream(s) on 2021-01-04T14:27:00Z : No. of bitstreams: 2 2-s2.0-84902077626.pdf: 3688371 bytes, checksum: 8ef2e3274cc65a66dafac711c841bef2 (MD5) 2-s2.0-84902077626.pdf.txt: 48326 bytes, checksum: 99d89c4fee89331c96a9c7924b82ef33 (MD5)en
dc.description.provenanceMade available in DSpace on 2015-11-26T15:04:55Z (GMT). No. of bitstreams: 2 2-s2.0-84902077626.pdf: 3610408 bytes, checksum: 85f65e26c9aba0540a28f80acce668d1 (MD5) 2-s2.0-84902077626.pdf.txt: 48326 bytes, checksum: 99d89c4fee89331c96a9c7924b82ef33 (MD5) Previous issue date: 2014en
dc.identifier.urihttp://www.repositorio.unicamp.br/handle/REPOSIP/87901
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/87901-
dc.identifier.idScopus2-s2.0-84902077626pt_BR
dc.description.referenceDainese, R.C., Remote sensing and geoprocessing applied to the temporary study of the landuse and in the comparison among unsupervised classification and visual analyses (2001) M.S. Thesis, State University of São Paulo School of Agronomic Sciences Botucatu, Brazilpt_BR
dc.description.referenceHeinzmann, U., Zollinger, G., Validation of representativeness with relief parameters based on the comparison of two landuse classifications (1995) CATENA, 24 (1), pp. 69-87. , Febpt_BR
dc.description.referenceHeinl, M., Walde, J., Tappeiner, G., Tappeiner, U., Classifiers vs. Input variables The drivers in image classification for land cover mapping (2009) Int. J. Appl. Earth Observ. Geoinf, 11 (6), pp. 423-430. , Decpt_BR
dc.description.referenceHaykin, S., (1994) Neural Networks: A Comprehensive Foundation, , Upper Saddle River, NJ, USA: Prentice-Hallpt_BR
dc.description.referenceBeekhuizen, J., Clarke, K.C., Toward accountable land use mapping: Using geocomputation to improve classification accuracy and reveal uncertainty (2010) Int. J. Appl. Earth Observ. Geoinf, 12 (3), pp. 127-137. , Junpt_BR
dc.description.referenceCohen, J., A coefficient of agreement for nominal scales (1960) Educ. Psychol. Meas, 20 (1), pp. 37-46. , Aprpt_BR
dc.description.referencePerumal, K., Bhaskaran, R., SVM-Based effective land use classification system for multispectral remote sensing images (2009) Int. J. Comp. Sci. Inf. Security, 6 (2), pp. 97-105pt_BR
dc.description.referenceKnorn, J., Rabe, A., Radeloff, V.V., Kuemmerle, T., Kozak, J., Hostert, P., Land cover mapping of large areas using chains classification of neighboring Landsat satellite images (2009) Remote Sens. Environ, 113 (5), pp. 957-964. , Maypt_BR
dc.description.referenceCortes, C., Vapnik, V., Support-vector networks (1995) Mach. Learn, 20 (3), pp. 273-297. , Seppt_BR
dc.description.referenceMing-Hseng, T., Sheng-Jhe, C., Gwo-Haur, H., Ming-Yu., S., A genetic algorithm rule-based approach for land-cover classification (2008) ISPRS J. Photogramm. Remote Sens, 63 (2), pp. 202-212. , Marpt_BR
dc.description.referenceKeuchel, J., Naumann, S., Heiler, M., Siegmund, A., Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data (2003) Remote Sensing of Environment, 86 (4), pp. 530-541. , DOI 10.1016/S0034-4257(03)00130-5pt_BR
dc.description.referenceZingaretti, P., Frontoni, E., Malinverni, E.S., Mancini, A., A hybrid approach to land cover classification from multi spectral images (2009) Proc. 15th Int. Conf. Image Anal. Process, Berlin, Germany, pp. 500-508pt_BR
dc.description.referenceJi., C.Y., Land-use classification of remotely sensed data using kohonen self-organizing feature map neural networks (2000) Photogramm. Eng. Remote Sens, 66 (12), pp. 1451-1460pt_BR
dc.description.referenceYuan, H., Wiele Der Van, C.F., Khorram, S., An automated artificial neural network system for land use/land cover classification from Landsat tm imagery (2009) Remote Sens, 1 (3), pp. 243-265pt_BR
dc.description.referenceGoncalves, M.L., Netto, M.L.A., Costa, J.A.F., Zullo Junior, J., An unsupervised method of classifying remotely sensed images using Kohonen self-organizing maps and agglomerative hierarchical clustering methods (2008) International Journal of Remote Sensing, 29 (11), pp. 3171-3207. , DOI 10.1080/01431160701442146, PII 792018039pt_BR
dc.description.referenceBo, S., Ding, L., Li, H., Di, F., Zhu, C., Mean shift-based clustering analysis of multispectral remote sensing imagery (2009) Int. J. Remote Sens, 30 (4), pp. 817-827pt_BR
dc.description.referenceComaniciu, D., Meer, P., Mean shift: A robust approach toward feature space analysis (2002) IEEE Trans. Pattern Anal. Mach. Intell, 24 (5), pp. 603-619. , Maypt_BR
dc.description.referenceShah, C.A., Varshney, P.K., Arora, M.K., Ica mixture model algorithm for unsupervised classification of remote sensing imagery (2007) Int. J. Remote Sens, 28 (8), pp. 1711-1731. , Janpt_BR
dc.description.referenceAri, C., Aksoy, S., Unsupervised classification of remotely sensed images using Gaussian mixture models and particle swarm optimization (2010) Proc IEEE Int. Geosci. Remote Sens. Symp, pp. 1859-1862pt_BR
dc.description.referenceKennedy, J., Eberhart, R.C., (2001) Swarm Intelligence, , San Francisco, CA, USA: Morgan Kaufmannpt_BR
dc.description.referenceHui, Y., Siamak, K., Dai X.Long, Applications of simulated annealing minimization technique to unsupervised classification of remotely sensed data (1999) International Geoscience and Remote Sensing Symposium (IGARSS), 1, pp. 134-136pt_BR
dc.description.referenceKirkpatrick, S., Gelatt, Jr.C.D., Vecchi, M.P., Optimization by simulated annealing (1983) Science, 220 (4598), pp. 671-680. , Maypt_BR
dc.description.referenceRocha, L.M., Cappabianco, F.A.M., Falcão, A.X., Data clustering as an optimum-path forest problem with applications in image analysis (2009) Int. J. Imag. Syst. Technol, 19 (2), pp. 50-68. , Junpt_BR
dc.description.referencePapa, J.P., Falcão, A.X., Suzuki, C.T.N., Supervised pattern classification based on optimum-path forest (2009) Int. J. Imag. Syst. Technol, 19 (2), pp. 120-131. , Junpt_BR
dc.description.referencePapa, J.P., Falão, A.X., A new variant of the optimum-path forest classifier (2008) Proc. 4th ISVC, pp. 935-944. , Berlin Germanypt_BR
dc.description.referenceComaniciu, D., An algorithm for data-driven bandwidth selection (2003) IEEE Trans. Pattern Anal. Mach. Intell, 25 (2), pp. 281-288. , Febpt_BR
dc.description.referenceShi, J., Malik, J., Normalized cuts and image segmentation (2000) IEEE Trans. Pattern Anal. Mach. Intell, 22 (8), pp. 888-905. , Augpt_BR
dc.description.referenceCormen, T., Leiserson, C., Rivest, R., (1990) Introduction to Algorithms, , Cambridge, U.K.: MIT Presspt_BR
dc.description.referenceAllène, C., Audibert, J.Y., Couprie, M., Cousty, J., Keriven, R., Some links between min-cuts, optimal spanning forests and watersheds (2007) Proc. MCT/INPE, pp. 253-264pt_BR
dc.description.referenceFeichtinger, H.G., Strohmer, T., (1997) Gabor Analysis and Algorithms: Theory and Applications, , 1st ed. Boston, MA, USA: Birkhauserpt_BR
dc.description.referencePapa, J.P., Suzuki, C.T.N., Falcão, A.X., (2009) LibOPF: A Library for the Design of Optimum-path Forest Classifiers, , http://www.ic.unicamp.br/afalcao/LibOPF, Software Version 2.0 Available At Online]. Available: Available atpt_BR
dc.description.referenceCollobert, R., Bengio, S., SVMTorch: Support vector machines for large-scale regression problems (2001) J. Mach. Learn. Res, 1, pp. 143-160. , Sept_BR
dc.description.referencePedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Duchesnay, E., Scikit-learn: Machine learning in python (2011) J. Mach. Learn. Res, 12, pp. 2825-2830pt_BR
dc.description.referenceKendall, M.G., A new measure of rank correlation (1938) Biometrika, 30 (1-2), pp. 81-93. , Junpt_BR
dc.description.referenceKuncheva, L.I., (2004) Combining Pattern Classifiers: Methods and Algorithms, , Hoboken, NJ, USA: Wileypt_BR
dc.description.referenceDavies, D.L., Bouldin, D.W., A cluster separation measure (1979) IEEE Trans. Pattern Anal. Mach. Intell Vol. PAMI-1, (2), pp. 224-227. , Aprpt_BR
dc.description.referencePapa, J.P., Cappabianco, F.A.M., Falcão, A.X., Optimizing optimum-path forest classification for huge datasets (2010) Proc. 20th Int. Conf. Pattern Recog, pp. 4162-4165pt_BR
dc.description.conferencenomept_BR
dc.contributor.departmentDepartamento de Sistemas de Informaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.subject.keywordLand cover classificationpt_BR
dc.subject.keywordOptimum-path forestpt_BR
dc.subject.keyword(OPF)pt_BR
dc.subject.keywordRemote sensingpt_BR
dc.identifier.source2-s2.0-84902077626pt_BR
dc.creator.orcid0000-0002-2914-5380pt_BR
dc.type.formArtigopt_BR
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