A new approach to identify land use and land cover areas in Brazilian Amazon areas using neural networks and IR-MSS fraction images from CBERS satellite

Author

Diverio, V.T. ; Formaggio, A.R. ; Shimabukuro, Y.E.

Author_Institution

Inst. Nacional de Pesquisas Espaciais, Sao Jose, Brazil

Volume

4

fYear

2003

fDate

21-25 July 2003

Firstpage

2553

Abstract

This paper shows the classification obtained with an artificial neural network to map land cover areas in Brazilian Amazon region. The new approach is based on fraction images generated by linear spectral mixture modeling and used as input to the network. It identified with good accuracy the following classes: water, deforested areas, forests, and areas without predominant forest physiognomy (savannah and regeneration areas).

Keywords

forestry; infrared imaging; neural nets; vegetation mapping; Brazilian Amazon areas; CBERS; China-Brazil Earth Resources Satellite; IR-MSS fraction images; Infra-Red Multispectral Scanner; artificial neural networks; deforested areas; forest physiognomy; land cover; linear spectral mixture modeling; neural networks; savannah; water; Artificial neural networks; Charge coupled devices; Charge-coupled image sensors; Image generation; Infrared spectra; Intelligent networks; Neural networks; Remote monitoring; Satellites; Water resources;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International

Print_ISBN

0-7803-7929-2

Type

conf

DOI

10.1109/IGARSS.2003.1294506

Filename

1294506