Registro Completo
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Biblioteca(s): |
Embrapa Roraima. |
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Data corrente: |
14/09/2021 |
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Data da última atualização: |
14/09/2021 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
PRUDENTE, V. H. R.; SANCHES, I. D.; ADAMI, M.; SKAKUN, S.; OLDONI, L. V.; XAUD, H. A. M.; XAUD, M. R.; ZHANG, Y. |
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Afiliação: |
HARON ABRAHIM MAGALHAES XAUD, CPAF-RR; MARISTELA RAMALHO XAUD, CPAF-RR. |
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Título: |
SAR data for land use land cover classification in a tropical region with frequent cloud cover. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
IGARSS - INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020. |
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Idioma: |
Inglês |
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Conteúdo: |
This study aims at mapping Land Use and Land Cover (LULC) in the region of Roraima, Brazil, using time-series of Sentinel-1 Synthetic Aperture Radar (SAR) data. All available Sentinel-1 images covering the study area were used and classified using two machine learning algorithms, namely random forest and multilayer perceptron. LULC heterogeneity with the SAR process complexity makes the process challenging in distinguishing certain classes. Results show that SAR data could be used for LULC mapping, as rainforest, savannas, water, and sandbank/outcrop classes. But cannot provide accurate separation for all classes, mainly for those with similar geometrical structures, such as regeneration areas, perennial crops, and buritizais. |
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Palavras-Chave: |
Machine learning; Sentinel. |
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Thesagro: |
Radar. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01409nam a2200229 a 4500
001 2134397
005 2021-09-14
008 2020 bl uuuu u00u1 u #d
100 1 $aPRUDENTE, V. H. R.
245 $aSAR data for land use land cover classification in a tropical region with frequent cloud cover.$h[electronic resource]
260 $aIGARSS - INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM$c2020
520 $aThis study aims at mapping Land Use and Land Cover (LULC) in the region of Roraima, Brazil, using time-series of Sentinel-1 Synthetic Aperture Radar (SAR) data. All available Sentinel-1 images covering the study area were used and classified using two machine learning algorithms, namely random forest and multilayer perceptron. LULC heterogeneity with the SAR process complexity makes the process challenging in distinguishing certain classes. Results show that SAR data could be used for LULC mapping, as rainforest, savannas, water, and sandbank/outcrop classes. But cannot provide accurate separation for all classes, mainly for those with similar geometrical structures, such as regeneration areas, perennial crops, and buritizais.
650 $aRadar
653 $aMachine learning
653 $aSentinel
700 1 $aSANCHES, I. D.
700 1 $aADAMI, M.
700 1 $aSKAKUN, S.
700 1 $aOLDONI, L. V.
700 1 $aXAUD, H. A. M.
700 1 $aXAUD, M. R.
700 1 $aZHANG, Y.
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Registro original: |
Embrapa Roraima (CPAF-RR) |
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