English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Este trabalho mostra os resultados iniciais de uma pesquisa sobre classificacao de imagens multiespectrais considerando feicoes de textura, aplicada ao mapeamento da cobertura da terra, com enfase na separacao das classes de cobertura vegetal. Para tanto foi efetuado um levantamento bibliografico e estudo sobre o assunto, que esta resumido na parte inicial. Na sequencia relata-se a parte pratica, onde foi feita a classificacao multiespectral da imagem LANDSAT-5 TM da Ilha de Sao Francisco do Sul-SC, utilizando o algoritmo de classificacao Maxima verossimilhanca. Para testar as potencialidades das feicoes de textura foram efetuadas quatro classificacoes distintas para obter as mesmas informacoes agrupadas em dez classes. Na primeira etapa foi efetuada somente a classificacao multiespectral, nas outras foram consideradas feicoes de textura e classificacao espectral. Classification of LANDSAT TMs multiespectral images and texture features: land cover mapping Abstract This paper shows the initial results of a research regarding multiespectral image classification using texture analysis for land cover maping. A bibliographic review was conducted wich is disposed in the first part of this work. Following this, a classification of the LANDSAT TM image of Sao Francisco island, SC, was performed using the Maximum Likelihood Method. To test the texture analysis potentialities, four distinct classifications were performed to obtain the same informations grouped into ten classes. In the first one only a multiespectral classification was performed, and in the other three the texture analysis was considered.

Classificação de imagens multiespectrais LANDSAT TM e feições de textura: mapeamento da cobertura da terra