elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

FRANCA - Developing an automated processing chain to quantify soil, green and dry vegetation in spaceborne imaging spectroscopy data

Ziel, Valentin and Bachmann, Martin (2018) FRANCA - Developing an automated processing chain to quantify soil, green and dry vegetation in spaceborne imaging spectroscopy data. IEEE whispers 2018, 23.-26.09.2018, Amsterdam, Niederlande.

[img] PDF - Registered users only
53MB

Abstract

In many previous studies linear spectral unmixing approaches have shown to be an accurate retrieval method for quantitative subpixel information. In addition, when the spectra of all endmembers (EM) are known, approaches based on multiple endmember spectral mixture analysis (MESMA) are highly accurate. For the retrieval of the EM spectra, spatial-spectral approaches such as the SSEE [ROGGE et al., 2007] were developed and successfully applied in many studies. But for many real-world applications with less-than-perfect EM knowledge, the retrieval accuracies are signifcantly lower. To overcome this problem, an automated MESMA methodology with an increased stability in case of inaccurate EM, or when the whole spectral variability cannot be completely represented by EMs, was developed [BACHMANN 2008, BACHMANN et al., 2009]. Also the combination of the SSEE endmember extraction and the uMESMA unmixing was already applied for soil-related application [MALEC et al., 2015; BAYER et al., 2016 ]. In preparation of the upcoming DESIS and EnMAP missions, an automated processing chain from including EM extraction, EM classifcation and MESMA unmixing is currently developed, which are based on the Python versions of SSSEE, uMESMA, and newly developed spectral classifers. The full chain is developed in Python3, and is using the Luigi workfow management in combination with a Docker-based distribution, therefore preparing for the improved availability of hyperspectral data in context of the upcoming hyperspectral spaceborne missions. Within this presentation, the overall processing chain design is presented, and details are given on the implementational and confgurational requirements for SSEE and uMESMA allowing for a fully automated and orchestrated processing.

Item URL in elib:https://elib.dlr.de/121933/
Document Type:Conference or Workshop Item (Speech)
Title:FRANCA - Developing an automated processing chain to quantify soil, green and dry vegetation in spaceborne imaging spectroscopy data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Ziel, Valentinvalentin.ziel (at) dlr.deUNSPECIFIED
Bachmann, Martinmartin.bachmann (at) dlr.deUNSPECIFIED
Date:September 2018
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:FRANCA, FCOVER, SSEE, MESMA, uMESMA, Python, Hyperspectral, Random Forest
Event Title:IEEE whispers 2018
Event Location:Amsterdam, Niederlande
Event Type:international Conference
Event Dates:23.-26.09.2018
Organizer:IEEE
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface
Deposited By: Ziel, Valentin
Deposited On:23 Oct 2018 12:22
Last Modified:23 Oct 2018 12:22

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.