%0 Journal Article
%3 freitas_modelo.pdf
%X The concept of spectral mixture offers a wide range of applications in the Remote Sensing area. The application of this concept, however, requires the prior estimation of the component's (endmembers) spectral response. This latter requirement can be achieved by different methods, as reported in the literature, such as techniques for the detection of pure pixels, use of spectral libraries, and field radiometric measurements. Among those, the most often used is the pure pixel approach. In this approach, the components' spectral reflectances are estimated by means of pixels covered entirely by a single component. This approach offers the advantage of allowing the extraction of the required spectral reflectance directly from the image data. This approach, however, becomes increasingly unfeasible as the spatial resolution of the image data decreases, due to the larger ground area covered by a single pixel. In this study we propose a methodology to estimate the spectral reflectance for each component class in moderate spatial resolution image data, by applying the linear mixing model (MLME), and higher spatial resolution image data as auxiliary data. It is expected that this methodology will provide a more practical way to implement the spectral mixture approach to moderate resolution image data, allowing in this way the expansion of the information about the components' proportions across larger areas, up-scaling information in regional and global studies. Experiments were carried out using CCD (20 m ground resolution) and IRMSS (80 m ground resolution) and WFI (260 m ground resolution) CBERS-2 image data, as medium and moderate spatial resolution data, respectively. The spectral reflectances for the components in the IRMSS and WFI CBERS-2 spectral bands are estimated by applying the proposed methodology. The reliability of the proposed methodology was assessed by both analyzing scatter plots for CBERS-2 data and by comparing the fraction images produced by image data sets of the sensors analyzed.
%@mirrorrepository sid.inpe.br/mtc-m21b/2013/09.26.14.25.22
%8 Jan.
%N 17
%9 journal article
%T Linear spectral mixture model in moderate spatial resolution image data / Modelo linear de mistura espectral em imagem de moderada resolução espacial
%@nexthigherunit 8JMKD3MGPCW/3ER446E
%K GEOBASE Subject Index: data set, image analysis, linearity, pixel, radiometric method, remote sensing, spatial resolution, spectral reflectance, CBERS-2.
%@usergroup Marciana
%@group DSR-OBT-INPE-MCT-BR
%@group
%@group DSR-OBT-INPE-MCT-BR
%F self-archiving-INPE-MCTI-GOV-BR
%2 sid.inpe.br/mtc-m21b/2016/10.04.11.48.12
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Universidade Federal do Rio Grande do Sul, Centro de Sensoriamento Remoto e Meteorologia, Caixa Postal - 15044, Porto Alegre, RS, Brazil
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%B Boletim de Ciências Geodésicas
%@versiontype finaldraft
%P 55-71
%4 sid.inpe.br/mtc-m21b/2016/10.04.11.48
%@documentstage not transferred
%D 2008
%V 14
%A Freitas, Ramon Morais de,
%A Haertel, V. b,
%A Shimabukuro, Yosio Edemir,
%@area SRE
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}