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TitleA method based on spatial and spectral information to reduce the solution space in endmember extraction algorithms
AuthorBeauchemin, M
SourceRemote Sensing Letters vol. 5, issue 5, 2014 p. 471-480
Alt SeriesEarth Sciences Sector, Contribution Series 20130425
PublisherTaylor & Francis
Mediapaper; on-line; digital
File formatpdf
Subjectsspectral analyses; remote sensing; local near-central (LNC); N-FINDR algorithm
Illustrationsgraphs; tables
ProgramRemote Sensing Science
Released2014 01 01
AbstractSpectral unmixing is a widely used approach for analysing hyperspectral images. This technique requires the knowledge of endmember spectral signatures that are commonly extracted from the observed data. Unfortunately, the computational complexity of current endmember extraction methods scales linearly with the number of pixels, which typically consists of the entire data set. In this paper, we propose a method to reduce the solution space for geometry-based endmember extraction algorithms. The nearest spectrum to the average spectra enclosed in non-overlapping windows is first selected. In the signal subspace, these spectra are located close to or at the centre of the data cloud enclosed within their respective window. We argue that, excepted for some peculiar situations, these local near-central (LNC) spectra cannot belong to data vertices where endmembers are expected to reside. We exploit this property to identify
a set of LNC spectra endmembers defining a simplex inscribed within the true endmember simplex. The simplex is determined using the N-FINDR algorithm. Spectra that are located outside the simplex defined by these LNC spectra endmembers represent the reduced pool of potential endmembers. Comparison with state-of-theart techniques on synthetic and real hyperspectral data indicates that the proposed
method provides equal or better levels of performance while maintaining good efficiency in terms of execution times.
Summary(Plain Language Summary, not published)
A new class of satellites has emerged which can acquire, within a single digital file, hundreds of images which focus on the same location of the earth¿s surface. Each of these images contains a different type of information. Such an image ensemble is called a hyperspectral image. Compared to more traditional satellite images, hyperspectral images are very large in size and therefore present unique challenges to extracting earth surface information using computer algorithms. In this study, a method has been developed to greatly reduce the amount of data to process in a hyperspectral image, by screening out redundant information. This results in more efficient, effective and rapid analysis of the data, which is important for the timely extraction of earth surface information.

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