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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.)

Examining region-based methods for land cover classification using stochastic distances

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Autor(es):
Negri, R. G. [1] ; Dutra, L. V. [2] ; Sant'Anna, S. J. S. [2] ; Lu, D. [3]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] UNESP Univ Estadual Paulista, Inst Ciencia & Tecnol, Sao Jose Campos, Sao Paulo - Brazil
[2] INPE, Div Proc Imagens, Sao Paulo - Brazil
[3] MSU, Ctr Global Change & Earth Observat, E Lansing, MI - USA
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: International Journal of Remote Sensing; v. 37, n. 8, p. 1902-1921, 2016.
Citações Web of Science: 2
Resumo

A recent alternative to standard pixel-based classification of remote-sensing data is region-based classification, which has proved to be particularly useful when analysing high-resolution imagery of complex environments, such as urban areas, or when addressing noisy data, such as synthetic aperture radar (SAR) images. First, following certain criteria, the imagery is decomposed into homogeneous regions, and then each region is classified into a class of interest. The usual method for region-based classification involves using stochastic distances, which measure the distances between the pixel distributions inside an unknown region and the representative distributions of each class. The class, which is at the minimum distance from the unknown region distribution, is assigned to the region and this procedure is termed stochastic minimum distance classification (SMDC). This study reports the use of methods derived from the original SMDC, Support Vector Machine (SVM), and graph theory, with the objective of identifying the most robust and accurate classification methods. The equivalent pixel-based versions of region-based analysed methods were included for comparison. A case study near the Tapajos National Forest, in Para state, Brazil, was investigated using ALOS PALSAR data. This study showed that methods based on the nearest neighbour, derived from SMDC, and SVM, with a specific kernel function, are more accurate and robust than the other analysed methods for region-based classification. Furthermore, pixel-based methods are not indicated to perform the classification of images with a strong presence of noise, such as SAR images. (AU)

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