@InProceedings{ReisDutrEsca:2017:SiMuMu,
author = "Reis, Mariane Souza and Dutra, Luciano Vieira and Escada, Maria
Isabel Sobral",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Simultaneous multi-source and multi-temporal land cover
classification using a Compound Maximum Likelihood classifier",
booktitle = "Anais...",
year = "2017",
editor = "Davis Jr., Clodoveu A. (UFMG) and Queiroz, Gilberto R. de (INPE)",
pages = "74--85",
organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 18. (GEOINFO)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "The most widely used change detection method is to classify remote
sensing images independently for each date, and stack them to form
a class sequence vector. However, impossible transitions within
the sequences might occur and errors might be accumulated. To
solve this, we propose a novel al- gorithm called Compound Maximum
Likelihood (CML), based on the Maximum Likelihood classifier (ML).
In CML information from all images is used jointly by considering
the a priori probability of each class sequence. The algorithm was
tested for Synthetic Aperture Radar and optical images
classification in a study area in Para \́ state, within the
Brazilian Amazon. CML presented either similar or very improved
accuracy index values over ML land cover classifica- tions.",
conference-location = "Salvador",
conference-year = "04-06 dez. 2017",
issn = "2179-4820",
language = "pt",
ibi = "8JMKD3MGPDW34P/3Q5DLCB",
url = "http://urlib.net/ibi/8JMKD3MGPDW34P/3Q5DLCB",
targetfile = "8reis_escada.pdf",
urlaccessdate = "11 maio 2024"
}