NDLI: Improved signal unmixing of vegetation using sparse group selection

Content Provider	IEEE Xplore Digital Library
Author	Iordache, M.-D. Tits, L. Somers, B. Plaza, A.
Copyright Year	2013
Description	Author affiliation: Centre for Remote Sensing & Earth Obs. Processes (TAP), Flemish Inst. for Technol. Res., Mol, Belgium (Iordache, M.-D.; Somers, B.) \|\| Dept. of Biosyst., Katholieke Univ. Leuven, Leuven, Belgium (Tits, L.) \|\| Dept. of Technol. of Comput. & Commun., Univ. of Extremadura, Caceres, Spain (Plaza, A.)
Abstract	Recently, signal unmixing was proposed in remote sensing with the goal to infer physical parameters of materials of interest, such as vegetation, on the ground. The typical approach uses large collections of pure spectra, called spectral libraries, in which many possible states of the vegetation are modeled by simulated or on-site acquired spectra. Spectra randomly selected from these libraries are used as input to dedicated unmixing methods, such as Multiple Endmember Spectral Mixture Analysis (MESMA). The spectra leading to the lowest reconstruction error are considered to be representative for the materials present in the pixel, such that the physical parameters of the ground vegetation can be inferred. However, the large number of spectra in the library imposes limits to the performance of this combinatorial approach, mainly related to running time constraints. In this paper, we propose the inclusion of a pre-processing step in the processing chain, based on the group lasso, which has the goal of selecting groups of signatures likely to be present in the mixtures. In this sense, the Group Sparse Unmixing via variable Splitting and Augmented Lagrangian (GSUnSAL) algorithm is used. The signatures contained in the groups selected by GSUnSAL are then used as input for MESMA. Our experiments using a real dataset acquired by an ASD spectrometer in a South-African orchard show that the proposed approach introduces important improvements in the signal unmixing solutions.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	3018
Ending Page	3021
File Size	94297
Page Count	4
File Format	PDF
ISBN	9781479911141
ISSN	21536996
DOI	10.1109/IGARSS.2013.6723461
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Libraries Soil Computer aided software engineering Materials Vectors Hyperspectral imaging spectral library Signal unmixing spectral unmixing sparse regression
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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