期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B7
页码：409-412
出版社：Copernicus Publications
摘要：Hyperspectral imaging sensors on environmental applications have high spectral resolution and low spatial resolution so that numerous disparate substances can contribute to the spectrum measured from a single pixel or in the field of view of the sensor. An important problem in Hyperspectral imaging processing is to decompose the mixed pixels into the materials that contribute to the pixel, endmember, and a set of corresponding fractions of the spectral signature in the pixel, abundances, and this problem is known as the unmixing problem. According to the definition, an endmember is an idealized pure signature of a class. Endmember extraction is one of the fundamental and crucial tasks in hyperspectral data exploitation. It has received considerable interest in recent years, with many researchers devoting their effort to develop algorithms for endmember extraction from hyperspectral data. An ultimate goal of an Endmember Extraction Algorithm (EEA) is to find the purest form of each spectrally distinct material on a scene. Endmember extraction tendency to the type of endmembers being derived, and the number of endmembers, estimated by an algorithm, with respect to the number of spectral bands, and the number of pixels being processed, also the required input data, and the kind of noise, if any, in the signal model surveying done. Identifying endmembers that satisfy both physical and mathematical imperatives is a considerable challenge, making autonomous endmember determination the hardest part of the unmixing problem. Of three stages that comprise unmixing, endmember determination is the most closely aligned with the material identification capabilities of unmixing. Non-statistical algorithms or Geometrical approach essentially assume the endmembers are deterministic quantities, whereas statistical approaches view endmembers as either deterministic, with an associated degree of uncertainty, or as fully stochastic, with random variables having probability density functions. In addition, specific features concerning the outputs, inputs, and noise models used by these algorithms are included according to the model specifically distinguishing the properties of endmember-determination algorithms. In this paper, Endmember Extraction Algorithms (EEAs) applied on a Hyperion image of southern of Tehran, IRAN. A large number of endmembers were suggested to enhance the classification accuracy while the seasonal variation in the spectral response should be taken into account in vegetation classification. We compare results of Geometrical approach in vegetation endmember extraction assistance with vegetation indices
