%0 Conference Proceedings
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@nexthigherunit 8JMKD3MGPCW/3ER446E
%@usergroup administrator
%3 classification of hyperspectral.pdf
%@secondarykey INPE-10450-PRE/5940
%A Carvalho Junior, Osmar Abílio de,
%A Carvalho, Ana Paula Ferreira de,
%A Guimarães, Renato Fontes,
%A Lopes, Richard Anderson Silva,
%A Guimarães, Paulo Honório,
%A Martins, Eder de Souza,
%A Pedreno, José Navarro,
%T Classification of Hyperspectral Image Using SCM Methods for Geobotanical Analysis in the Brazilian Savanna Region
%B International Geoscience and Remote Sensing Symposium, (IGARSS).
%C Toulouse
%D 2003
%P 3754-3756
%S Proceedings
%8 21-25 July 2003
%2 sid.inpe.br/jeferson/2004/01.13.13.21.32
%4 sid.inpe.br/jeferson/2004/01.13.13.21
%K hyperspectral; Brazilian Savanna; Spectral Correlation
%X This work presents a spectral analysis of natural targets behavior of Cerrado using an AVIRIS sensor image (Airborne Visible/InfraRed Imaging Spectrometer). The sensor AVIRIS was brought to Brazil in 1995 in the SCAR-B (Smoke, Clouds and Radiation - Brazil) mission with the purpose of evaluating atmospheric effects. This activity was accomplished by NASA, INPE (National Institute of Space Research) and AEB (Brazilian Space Agency). During the SCAR-B mission, the AVIRIS sensor acquired image of Goiás region, in August 16th 1995. The image used in this study presents geological structures that mark effects on the distribution of vegetation types. The aim of this work was to adapt and to test the employment of the spectral analysis in an AVIRIS hyperspectral image to differentiate vegetation patterns in order to compare with the geological structure. The atmospheric correction was performed using ATREM method (Atmosphere Removal Program) complemented by EFFORT (Empirical Flat Field Optical Reflectance Transformation) method. The endmembers were detected after the atmospheric correction according to the following steps: a) spectral reduction by the Minimum Noise Fraction (MNF) transformation, b) spatial reduction by the Pixel Purity Index (PPI) and c) manual identification of the members using the N-dimensional visualization device. The employment of this technique allowed selecting a spectral series related to vegetation. The spectral analysis showed three main groups: a) photosynthetic vegetation (PV), b) nonphotosynthetic vegetation (NPV) and c) burned vegetation. The spectral classification was done using the SCM (Spectral Correlation Mapper) method. The SCM algorithm is a spectral classifier that presents advantages over Spectral Angle Mapper and Spectral Feature Fitting methods due to the ability to detect false positive results. The SCM was performed in the study image using the selected endmember means (photosynthetic active vegetation, NPV and burned areas). In classified images the most similar areas presented lighter colors, while the correlated areas were darkened. The color composition of the SCM classified images allowed the detection of differences in the vegetation distribution, enhancing structural and geomorphologic features of the study site.
%@language English
%@copyholder SID/SCD
%@secondarytype PRE CI
%@dissemination <E>
%@area SRE
%@group DSR-INPE-MCT-BR,
%@affiliation UnB, Brasília - DF,
%@affiliation  Embrapa/CPAC, Planaltina - DF,
%@affiliation Universidade Miguel Hernández de Elche, Espanha.,