Indexing metadata

Mapping Land Cover Classes in the Southern State of Rio Grande Do Sul, Brazil, Using Multiple Endmember Spectral Mixture Analysis (MESMA) Model Applied to Hyperion/EO-1 Hyperspectral Data


 
Dublin Core PKP Metadata Items Metadata for this Document
 
1. Title Title of document Mapping Land Cover Classes in the Southern State of Rio Grande Do Sul, Brazil, Using Multiple Endmember Spectral Mixture Analysis (MESMA) Model Applied to Hyperion/EO-1 Hyperspectral Data
 
2. Creator Author's name, affiliation, country Rodrigo de Marsillac Linn; SMUrb, Secretaria Municipal de Urbanismo, PMPA, Prefeitura Municipal de Porto Alegre, RS.
 
2. Creator Author's name, affiliation, country Silvia Beatriz Alves Rolim; CEPSRM/UFRGS, Centro Estadual De Pesquisas em Sensoriamento Remoto e Meteorologia, Federal University of Rio Grande do Sul State, Brazil
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) Spectral Mixture Analysis; Spectral Classification; Spectral Library; Hyperspectral Data; Image Processing; Signatures
 
4. Description Abstract

In this paper we evaluated the potential use of Multiple Endmember Spectral Mixture Analysis (MESMA) applied to EO-1 Hyperion hyperspectral data to separate land covers (soil = dunes and dry fields; green vegetation = pinus, eucalyptus and grasslands; water = without sediments, with sediments, and with chlorophyll; and shade), in the southern state of Rio Grande do Sul, Brazil. The approach involved (a) preprocessing and atmospheric correction of Hyperion image; (b) sequential use of Minimum Noise Fraction (MNF), Pixel Purity Index (PPI) and n-Dimensional Visualizer techniques in the visible to shortwave range for the initial selection of a group of endmember, and another group of pixels for model validation; (c) use of the software Visualization and Image Processing for Environmental Research (VIPER) Tools to perform the final selection of endmembers based on the spectral library, and to obtain MESMA models; and (d) evaluation of resulting fraction images and RMSE values to determine the optimal number of endmembers of the MESMA model. Results showed that a four-endmember MESMA model described the diversity of the scene components, including that of materials within the same class (e.g., pinus and eucalyptus) and produced the largest fractions and the lowest RMSE values on a per-pixel basis. Results also showed the performance of MESMA applied to Hyperion data to discriminate properly land covers in the coastal plains, even considering the low signal-to-noise ratio of the instrument.

 
 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s)
 
7. Date (YYYY-MM-DD) 2015-04-29
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type
 
9. Format File format PDF
 
10. Identifier Uniform Resource Identifier http://technical.cloud-journals.com/index.php/IJARSG/article/view/Tech-375
11. Source Journal/conference title; vol., no. (year) International Journal of Advanced Remote Sensing and GIS; Volume 4 (Year 2015)
 
12. Language English=en en
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
 
15. Rights Copyright and permissions

Copyright Terms & Conditions

Authors who publish with this journal agree to the following terms:

a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work

Cloud Publications reserves the right to amend/change the copyright policy; with/without notice.