Name
Abstract | ||
|---|---|---|
As an important indicator of vegetation moisture status, Fuel Moisture Content (FMC) is commonly used for predicting vulnerability to wild fire. Currently, the FMC estimation using spectral data is mainly based on spectral indices derived from several bands and these methods do not make full use of the entire spectrum. Partial Least Square (PLS) is a new multivariate statistical method which can effectively reduce collinearity. In this paper, using LOPEX dataset, we mainly explored the performance of PLS coupled with different feature selection methods for FMC retrieval. According to the results, PLS shows great potential to extract FMC from spectral data; when coupled with different band selection approaches, the models also generate high estimation precision; with band selection, the PLS coupled models involved fewer bands, lowering the model complexity. Thus, the high estimation precision and much simpler modeling make band selection-PLS coupled methods superior to original PLS for FMC retrieval. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IGARSS.2010.5652617 | IGARSS |
Keywords | Field | DocType |
multivariate statistical method,fmc retrieval,pls,hyperspectral,collinearity,partial least square,forestry,fires,vegetation moisture status,information retrieval,fmc estimation,retrieval,fuel moisture content,least squares approximations,hyperspectral data,wild fire vulnerability,moisture measurement,lopex dataset,band selection,vegetation mapping,vegetation,feature selection methods,multivariate statistics,reflectivity,calibration,estimation,spectrum,correlation,feature selection,moisture | Least squares,Data mining,Collinearity,Moisture,Feature selection,Computer science,Multivariate statistics,Remote sensing,Hyperspectral imaging,Water content,Calibration | Conference |
ISSN | ISBN | Citations |
2153-6996 E-ISBN : 978-1-4244-9564-1 | 978-1-4244-9564-1 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Zhang | 1 | 12 | 3.78 |
| Jianjun Wu | 2 | 5 | 0.76 |
| Lei Zhou | 3 | 155 | 26.38 |