Paper
10 November 2004 Efficient methodology for endmembers selection by field radiometry: an application to multispectral mixture model
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Proceedings Volume 5573, Image and Signal Processing for Remote Sensing X; (2004) https://doi.org/10.1117/12.565233
Event: Remote Sensing, 2004, Maspalomas, Canary Islands, Spain
Abstract
Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multispectral imagery. It aims to identify a set of reference spectra named endmembers that can be used to model the spectral response for each pixel of the remote image. Thus, the modelling is usually carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for large hyperspectral dataset subpixel analysis, few methods are available in the literature about the optimal selection of endmembers through field spectroscopy as well as the applied regression analysis techniques over the model obtained. This work has as main objective to deal with these aspects. With regard to the first subject mentioned and in order to determine not only specific conditions about covers (health, contamination, geographic and geologic characteristics, etc.), but to assure an efficient sampling method, ground-truth data collection and description is still an essential task. In particular there is a very important question to improve: the determination of the samples number to pick up in terms of the vegetation types. On this way, a useful statistic, based on t-Student distribution will be discussed in this paper.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jose Manuel Vazquez, Agueda Arquero, Estibaliz Martinez, and Consuelo Gonzalo "Efficient methodology for endmembers selection by field radiometry: an application to multispectral mixture model", Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004); https://doi.org/10.1117/12.565233
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Cited by 2 scholarly publications.
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KEYWORDS
Statistical analysis

Radiometry

Data modeling

Roads

Sensors

Vegetation

Remote sensing

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