Parameter comparison for linear spectral unmixing in field hyperspectral sampling of rocky desertification

2018 ◽  
Author(s):  
Jiang Miao ◽  
Yi Lin
Keyword(s):  
Spectral Unmixing ◽  
Rocky Desertification ◽  
Linear Spectral Unmixing

Lithologic variation within bright material on Vesta revealed by linear spectral unmixing

Icarus ◽  
2016 ◽  
Vol 272 ◽  
pp. 16-31 ◽  
Author(s):  
F. Zambon ◽  
F. Tosi ◽  
C. Carli ◽  
M.C. De Sanctis ◽  
D.T. Blewett ◽  
...  
Keyword(s):  
Spectral Unmixing ◽  
Linear Spectral Unmixing

Using Multispectral Imagery and Linear Spectral Unmixing Techniques for Estimating Crop Yield Variability

2007 ◽  
Vol 50 (2) ◽  
pp. 6676-674 ◽  
Author(s):  
C. Yang ◽  
J. H. Everitt ◽  
J. M. Bradford
Keyword(s):  
Crop Yield ◽  
Spectral Unmixing ◽  
Yield Variability ◽  
Multispectral Imagery ◽  
Linear Spectral Unmixing

scholarly journals Propuesta de un Índice de Bofedal para la Teledetección de Bofedales en Cabeceras de Cuenca Usando Datos Imágenes de los Sensores TM, OLI a bordo de los Satélites Landsat - Caso Estudio: Bofedal Chunal, Cuenca Alta del río Chillón

2020 ◽  
Vol 24 (2) ◽  
pp. 23-30
Author(s):  
Jorge García ◽  
Jhon Guerrero ◽  
Bram Willems ◽  
Raul Espinoza
Keyword(s):  
Google Earth ◽  
Spectral Unmixing ◽  
Linear Spectral Unmixing ◽  
Del Rio

Esta investigación propone un Índice de Bofedal (IB) para identificar los bofedales, ubicados sobre los 3800 ms.n.m. La propuesta del IB es un resumen de la tesis de maestría de Garcia Dulanto, (2018) y se fundamenta en dos métodos: el primero basado en la clasicación Linear Spectral Unmixing queemplea firmas espectrales seleccionadas de elementos característicos del área de estudio. Se seleccionó firmas espectralmente ideales (endmember, EM) para representar a : bofedales (EM bofedal), rocas (EM roca) y suelo desnudo (EM suelo). El segundo método está basado en los índices o parámetrosbiofísicos NDVI, NDWI y NDII. La combinación en imagen RGB: NDII, NDVI, NDWI muestra los bofedales en el área de estudio en tonos amarillos. Se integran los dos métodos usando la correlación de Pearson entre la fracción del endmember-bofedal y de los bofedales. Se obtiene máxima y mínima correlación con los índices NDWI y NDII. Con estos índices se propone un índice IB = (NDWI - NDII)/(NDWI + NDII) para zonicar de manera directa los bofedales. El IB fue validado mediante las imágenes de alta resolución de Google Earth, obteniendo un acierto de 98.36 %.

Detecting fluorescent protein expression and co-localisation on single secretory vesicles with linear spectral unmixing

2006 ◽  
Vol 35 (6) ◽  
pp. 533-547 ◽  
Author(s):  
Fabien Nadrigny ◽  
Isabelle Rivals ◽  
Petra G. Hirrlinger ◽  
Annette Koulakoff ◽  
Léon Personnaz ◽  
...  
Keyword(s):  
Protein Expression ◽  
Fluorescent Protein ◽  
Spectral Unmixing ◽  
Secretory Vesicles ◽  
Linear Spectral Unmixing

Detection of Urban Expansion by Using DMSP-OLS, Landsat Data and Linear Spectral Unmixing Method

2019 ◽  
pp. 1372-1382
Author(s):  
Cihan Uysal ◽  
Derya Maktav
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Urban Expansion ◽  
Dynamic Structure ◽  
Spectral Unmixing ◽  
Landsat Images ◽  
Green Vegetation ◽  
Linear Mixture Model ◽  
Linear Spectral Unmixing ◽  
Spectral Mixture

Urbanization has been increasingly continuing in Turkey and in the world for the last 30 years. Especially for the developing countries, urbanization is a necessary fact for the sustainability of the urban growth. Yet, this growth should be controlled and planned; otherwise, many environmental problems might occur. Therefore, the urban areas having dynamic structure should be monitored periodically. Monitoring the changes in urban environment can be provided with land cover land use (LCLU) maps produced by the pixel based classification methods using ‘maximum likelihood' and ‘isodata' techniques. However, these thematic maps might bring about inaccurate classification results in heterogeneous areas especially where low spatial resolution satellite data is used since, in these approaches, each pixel is represented with only one class value. In this study, considering the spectral mixture analysis (SMA) each pixel is represented by endmember fractions. The earth is represented more accurately using 'substrate (S)', ‘green vegetation (V)' and ‘dark surfaces (D)' spectral endmember reflectances with this analysis based on linear mixture model. Here, the surrounding of Izmit Gulf, one of the most industrialized areas of Turkey, has been chosen as the study area. SMA has been applied to LANDSAT images of the years of 1984, 1999 and 2009. In addition, DMSP-OLS data of 1992, 1999 and 2009 has been used to detect urban areas. According to the results, the changes in LCLU and especially the urban growth areas have been detected accurately using the SMA method.

Collinearity and orthogonality of endmembers in linear spectral unmixing

2012 ◽  
Vol 18 ◽  
pp. 491-503 ◽  
Author(s):  
Freek D. Van der Meer ◽  
Xiuping Jia
Keyword(s):  
Spectral Unmixing ◽  
Linear Spectral Unmixing
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