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Conference Paper: Geological applications of machine learning on hyperspectral remote sensing data
Title | Geological applications of machine learning on hyperspectral remote sensing data |
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Authors | |
Keywords | Cluster classification CRISM Hyperspectral Unsupervised learning |
Issue Date | 2015 |
Publisher | SPIE - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml |
Citation | Image Processing: Machine Vision Applications VIII (Conference 9405), San Francisco, CA., 10-11 February 2015. In Proceedings of SPIE, 2015, v. 9405, article no. 940512, p. 1-6 How to Cite? |
Abstract | The CRISM imaging spectrometer orbiting Mars has been producing a vast amount of data in the visible to infrared wavelengths in the form of hyperspectral data cubes. These data, compared with those obtained from previous remote sensing techniques, yield an unprecedented level of detailed spectral resolution in additional to an ever increasing level of spatial information. A major challenge brought about by the data is the burden of processing and interpreting these datasets and extract the relevant information from it. This research aims at approaching the challenge by exploring machine learning methods especially unsupervised learning to achieve cluster density estimation and classification, and ultimately devising an efficient means leading to identification of minerals. A set of software tools have been constructed by Python to access and experiment with CRISM hyperspectral cubes selected from two specific Mars locations. A machine learning pipeline is proposed and unsupervised learning methods were implemented onto pre-processed datasets. The resulting data clusters are compared with the published ASTER spectral library and browse data products from the Planetary Data System (PDS). The result demonstrated that this approach is capable of processing the huge amount of hyperspectral data and potentially providing guidance to scientists for more detailed studies. |
Persistent Identifier | http://hdl.handle.net/10722/211420 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.152 |
DC Field | Value | Language |
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dc.contributor.author | Tse, CH | - |
dc.contributor.author | Li, Y | - |
dc.contributor.author | Lam, EY | - |
dc.date.accessioned | 2015-07-13T03:24:33Z | - |
dc.date.available | 2015-07-13T03:24:33Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Image Processing: Machine Vision Applications VIII (Conference 9405), San Francisco, CA., 10-11 February 2015. In Proceedings of SPIE, 2015, v. 9405, article no. 940512, p. 1-6 | - |
dc.identifier.isbn | 978-162841495-0 | - |
dc.identifier.issn | 0277-786X | - |
dc.identifier.uri | http://hdl.handle.net/10722/211420 | - |
dc.description.abstract | The CRISM imaging spectrometer orbiting Mars has been producing a vast amount of data in the visible to infrared wavelengths in the form of hyperspectral data cubes. These data, compared with those obtained from previous remote sensing techniques, yield an unprecedented level of detailed spectral resolution in additional to an ever increasing level of spatial information. A major challenge brought about by the data is the burden of processing and interpreting these datasets and extract the relevant information from it. This research aims at approaching the challenge by exploring machine learning methods especially unsupervised learning to achieve cluster density estimation and classification, and ultimately devising an efficient means leading to identification of minerals. A set of software tools have been constructed by Python to access and experiment with CRISM hyperspectral cubes selected from two specific Mars locations. A machine learning pipeline is proposed and unsupervised learning methods were implemented onto pre-processed datasets. The resulting data clusters are compared with the published ASTER spectral library and browse data products from the Planetary Data System (PDS). The result demonstrated that this approach is capable of processing the huge amount of hyperspectral data and potentially providing guidance to scientists for more detailed studies. | - |
dc.language | eng | - |
dc.publisher | SPIE - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml | - |
dc.relation.ispartof | Proceedings of SPIE | - |
dc.rights | Copyright 2015 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited. This article is available online at https://doi.org/10.1117/12.2178400 | - |
dc.subject | Cluster classification | - |
dc.subject | CRISM | - |
dc.subject | Hyperspectral | - |
dc.subject | Unsupervised learning | - |
dc.title | Geological applications of machine learning on hyperspectral remote sensing data | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Li, Y: yiliang@hku.hk | - |
dc.identifier.email | Lam, EY: elam@eee.hku.hk | - |
dc.identifier.authority | Li, Y=rp01354 | - |
dc.identifier.authority | Lam, EY=rp00131 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1117/12.2178400 | - |
dc.identifier.scopus | eid_2-s2.0-84926667819 | - |
dc.identifier.hkuros | 245308 | - |
dc.identifier.volume | 9405 | - |
dc.identifier.spage | article no. 940512, p. 1 | - |
dc.identifier.epage | article no. 940512, p. 6 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 150713 | - |
dc.identifier.issnl | 0277-786X | - |