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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Hierarchical clustering and stochastic distance for indirect semi-supervised remote sensing image classification

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Autor(es):
Sapucci, Gabriela Ribeiro [1] ; Negri, Rogerio Galante [1]
Número total de Autores: 2
Afiliação do(s) autor(es):
[1] Univ Estadual Paulista, ICT, UNESP, Sao Jose Dos Campos, SP - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: SN APPLIED SCIENCES; v. 1, n. 3 MAR 2019.
Citações Web of Science: 0
Resumo

Usually, image classification methods have supervised or unsupervised learning paradigms. While unsupervised methods do not need training data, the meanings behind the classified elements are not explicitly know. Conversely, supervised methods are able to provide classification results with an intrinsic meaning, since a labeled dataset is available for training, which may be a limitation in some cases. The semi-supervised learning paradigm, which simultaneously exploits both labeled and unlabeled data, may be an alternative to this dilemma. This work proposes a semi-supervised classification framework through the combination of the Hierarchical Divisive Algorithm and stochastic distance concepts, where the former is adopted to automatically determine clusters in the data and the latter is used to label such clusters in a supervised way. In order to verify the potential of the proposed framework, two case studies about land use and land cover classification were carried out in an Amazonian area using synthetic aperture radar and multispectral data acquired by ALOS PALSAR and LANDSAT-5 TM sensors. Supervised methods based on statistical concepts were also included in these studies as baselines. The results show that when very small training sets are available, the proposed method provides results up to 14.6% and 3.8% more accurate than the baselines with respect to the classification of TM and PALSAR images, respectively. (AU)

Processo FAPESP: 18/01033-3 - Investigação e desenvolvimento de algoritmos para detecção de mudança em imagens de sensoriamento remoto
Beneficiário:Rogério Galante Negri
Linha de fomento: Auxílio à Pesquisa - Regular
Processo FAPESP: 16/06242-4 - Classificação de imagens de sensoriamento remoto via rotulação de agrupamentos por distâncias estocásticas
Beneficiário:Gabriela Ribeiro Sapucci
Linha de fomento: Bolsas no Brasil - Iniciação Científica
Processo FAPESP: 14/14830-8 - Estudo e desenvolvimento de novas funções Kernel com aplicações em classificação de imagens de sensoriamento remoto
Beneficiário:Rogério Galante Negri
Linha de fomento: Auxílio à Pesquisa - Regular