Mapping of urban typical land covers using GaoFen-5 data: linear spectral unmixing based on a novel spectral analysis technique

2021 ◽  
Author(s):  
Hui Huang ◽  
Yi Jing
Keyword(s):  
Spectral Analysis ◽  
Spectral Unmixing ◽  
Analysis Technique ◽  
Linear Spectral Unmixing ◽  
Land Covers

scholarly journals IMPROVING LINEAR SPECTRAL UNMIXING THROUGH LOCAL ENDMEMBER DETECTION

2015 ◽  
Vol XL-3/W2 ◽  
pp. 177-181 ◽  
Author(s):  
R. Ramak ◽  
M. J. Valadan Zouj ◽  
B. Mojaradi
Keyword(s):  
Spectral Analysis ◽  
Dimension Reduction ◽  
Linear Combination ◽  
Computation Time ◽  
Physical Nature ◽  
Spectral Unmixing ◽  
Mathematical Technique ◽  
Data Set ◽  
Mixed Pixels ◽  
Linear Spectral Unmixing

There are a considerable number of mixed pixels in remotely sensed images. Different sub-pixel analyses have been recently developed correspondingly. A well-known method is linear spectral unmixing which obtains an abundance of each endmember in a given pixel. This model assumes that each pixel is a linear combination of all endmembers in a scene. This assumption is not correct since each pixel can only be a composition of some surrounding endmembers. Even though, a fully mathematical technique is used for spectral analysis, the output of the model may not represent the physical nature of the objects over the pixel under test. In this regard, this paper proposes a Local Linear Spectral Unmixing which is based on neighbor pixels classes. Having classified the image, using a supervised classifier, it is scanned through a window of an appropriate size. For each pixel at the center of the window, the endmember matrix is formed only based on the majority classes existed in the window. Then the amount of each one is calculated. The LLSU method was evaluated on an AVIRIS data set collected from an agricultural area of northern Indiana. The results of the proposed method demonstrate a significant improvement in comparison with the LSU results. Moreover, due to the dimension reduction of the endmember matrix in this method, the computation time of the LLSU speeds up by three to eight times compared to the conventional Linear Spectral Unmixing method. As a result, the proposed method is efficient over the spectral unmixing tasks.

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

scholarly journals An Improved Method for Discerning Broken Rotor Bar Fault and Load Oscillation in Induction Motors

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3130 ◽  
Author(s):  
Liling Sun ◽  
Boqiang Xu
Keyword(s):  
Spectral Analysis ◽  
Theoretical Basis ◽  
Induction Motors ◽  
The Other ◽  
Improved Method ◽  
Broken Rotor Bar ◽  
Signal Parameters ◽  
Analysis Technique ◽  
Novel Strategy ◽  
The One

A few methods for discerning broken rotor bar (BRB) fault and load oscillation in induction motors have been reported in the literature. However, they all perhaps inevitably fail in adverse cases in which these two phenomena are simultaneously present. To tackle this problem, an improved method for discerning BRB fault and load oscillation is proposed in this paper based on the following work. On the one hand, the theoretical basis is analytically extended to include such an adverse case, yielding some important findings on the spectra of the instantaneous reactive and active powers. A novel strategy is thus outlined to correctly discern BRB fault and load oscillation even when simultaneously present. On the other hand, Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) is adopted as the spectral analysis technique to deal with the instantaneous reactive and active powers, yielding a certain improvement compared to the existing methods, adopting Fast Fourier Transform (FFT). Simulation and experimental results demonstrate that the proposed method can correctly discern BRB fault and load oscillation even when simultaneously present.

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.

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