Name
Abstract | ||
|---|---|---|
Lunar minerals mapping is one of basic aims of China's Lunar Exploration Program. The goal of this paper was to use multiple mineral mapping techniques including classification and spectral matching for lunar surface minerals mapping and choose the effective methods based on the image data which was simulated by 76 lunar samples spectra supplied by LSCC. The results indicated that Mahalanobis Distance and support vector machine performs best of the supervised classification methods. SAM is more effective than SID of the spectral matching methods. The classification capability was different for the different size samples of the same materials. The samples with obvious diagnosed spectral characteristic can be identified effectively. Those without diagnosed spectral characteristic are sensitive to the mapping method. Besides the mapping methods, there are other factors which may affect the mapping results, such as the lunar soil component, the lunar soil maturity, the particle size and the data preprocessing procedure. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IGARSS.2009.5417859 | IGARSS |
Keywords | Field | DocType |
data preprocessing procedure,lunar soil component,lunar rocks,image simulation,supervised classification methods,lunar exploration program,astronomical image processing,lunar soil maturity,simulated data,lunar surface minerals mapping,spectral matching method,particle size,lunar surface,image classification,support vector machine,mahalanobis distance,multiple mineral mapping techniques,data preprocessing,remote sensing,iron,moon,hyperspectral imaging,geoscience,earth,spectroscopy | Astronomical image processing,Lunar soil,Computer science,Remote sensing,Support vector machine,Data pre-processing,Mahalanobis distance,Hyperspectral imaging,Contextual image classification,Spectral matching | Conference |
Volume | ISSN | ISBN |
3 | 2153-6996 | 978-1-4244-3395-7 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haixia He | 1 | 0 | 0.68 |
| Bing Zhang | 2 | 422 | 74.10 |
| Zhengchao Chen | 3 | 22 | 10.85 |
| Ru Li | 4 | 6 | 0.90 |