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Title: Extended linear mixing model in an ecosytem with high spectral variability
Authors: Ibarrola-Ulzurrun, Edurne 
Drumetz, Lucas
Chanussot, Jocelyn
Gonzalo Martin,Consuelo 
Marcello, Javier 
UNESCO Clasification: 250616 Teledetección (Geología)
Keywords: Ecosystem Management
Hyperspectral Imagery
Spectral Unmixing
Spectral Variability
Mixture Analysis
Endmember Variability
Issue Date: 2018
Journal: IEEE International Geoscience and Remote Sensing Symposium proceedings 
Conference: 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 
Abstract: Hyperspectral imagery (HSI) has become an important tool in ecosystem conservation due to its capability to perform accurate spectral unmixing, for vegetation mapping and ecosystem monitoring. An issue to be solved is the spectral variability of endmembers that can be induced by sensor noise and topographic changes. This spectral variability is considered by the Extended Linear Mixing Model (ELMM), which is applied to a mountainous ecosystem with high spectral variability and radiometric changes in each swath. The results obtained are very satisfactory, achieving reasonable abundance estimations and accurate characterization of the variability within the scene. ELMM allows studying the features of each pixel, including additional information about the characterization of the mixed pixels, in HSI by taking spectral variability into account. Moreover, it is observed that ELMM is robust to the absence of pure pixels as well as to noise.
ISBN: 9781538671504
ISSN: 2153-6996
DOI: 10.1109/IGARSS.2018.8519128
Source: International Geoscience and Remote Sensing Symposium (IGARSS) [ISSN 2153-6996], v. 2018-July, p. 2685-2688
Appears in Collections:Actas de congresos
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