The Feature Selection and Extraction of Hyperspectral Mineralization Information Based on Rough Sets Theory

Zhan, Yunjun; Hu, Guanyu; Wu, Yue

doi:10.1109/paciia.2008.35

Cited by 3 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this way, many types of ore-forming and orecontrolling elements can be reasonably determined. Research into the spectral combination, spectral testing, and spectral inversion of remote sensing data can be used to enhance and extract specific rock types, mineralization alteration types, and mineral types, and to improve high-resolution remote sensing prospecting technology and obtain research results that can be used for rapid and low investment [96]. Furthermore, combining geology with health, linking pollution research with environmental geology, or relating remote sensing to biogeochemistry are all worthy research fields [97], which could lead to better coupling of remote sensing surface information and geophysical data.…”

Section: Multidisciplinary Approachesmentioning

confidence: 99%

A Review of Geological Applications of High-Spatial-Resolution Remote Sensing Data

Xiao

Chen

et al. 2018

Preprint

View full text Add to dashboard Cite

Geologists employ high-spatial-resolution (HR) remote sensing (RS) data for many diverse applications as they effectively reflect detailed geological information, enabling high-quality and efficient geological surveys. Applications of HR RS data to geological and related fields have grown recently. By analyzing these applications, we can better understand the results of previous studies and more effectively use the latest data and methods to efficiently extract key geological information. HR optical remote sensing data are widely used in geological hazard assessment, seismic monitoring, mineral exploitation, glacier monitoring, and mineral information extraction due to high accuracy and clear object features. Compared with optical satellite images, synthetic-aperture radar (SAR) images are stereoscopic and exhibit clear relief, strong performance, and good detection of terrain, landforms, and other information. SAR images have been applied to seismic mechanism research, volcanic monitoring, topographic deformation, and fault analysis. Furthermore, a multi-standard maturity analysis of the geological applications of HR images using literature from the Science Citation Index reveals that optical remote sensing data are superior to radar data for mining, geological disaster, lithologic, and volcanic applications, but inferior for earthquake, glacial, and fault applications. Therefore, geological remote sensing research needs to be truly multidisciplinary or interdisciplinary, ensuring more detailed and efficient surveys through cross-linking with other disciplines. Moreover, the recent application of deep learning technology to remote sensing data extraction has improved automatic processing and data analysis capabilities.

show abstract

Section: Multidisciplinary Approachesmentioning

confidence: 99%

A Review of Geological Applications of High-Spatial-Resolution Remote Sensing Data

Xiao

Chen

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…For the sake of further discussion, we introduce some basic concepts of rough sets at first [5][6][7].…”

Section: Basic Theory Of Rough Setsmentioning

confidence: 99%

Freeway Traffic Flow Model Based on Rough Sets and Elman Neural Network

Liang

Fan

2009

2009 International Conference on Information Engineering and Computer Science

View full text Add to dashboard Cite

Rough sets theory is a new tool for processing fuzzy and uncertain knowledge, and has already been applied to many areas successfully. In this paper, a freeway traffic flow model based on rough sets and Elman neural network is put forward. The main idea of this approach is that some redundant features of sample data are reduced by rough sets firstly, then Elman neural network is used to build traffic flow model. Finally, a freeway with five segments, one on-ramp and one off-ramp is simulated. It is proved that the combined model of rough sets and Elman neural network has higher accuracy and better associational output ability than Elman neural network model by comparing their simulation outputs. The high performance of this combined model provides a novel and practical way to realize on-line modeling of freeway traffic flow.

show abstract

A Review of Geological Applications of High-Spatial-Resolution Remote Sensing Data

Xiao

Chen

et al. 2019

J CIRCUIT SYST COMP

View full text Add to dashboard Cite

Geologists employ high-spatial-resolution (HR) remote sensing (RS) data for many diverse applications as they effectively reflect detailed geological information, enabling high-quality and efficient geological surveys. Applications of HR RS data to geological and related fields have grown recently. HR optical remote sensing data are widely used in geological hazard assessment, seismic monitoring, mineral exploitation, glacier monitoring, and mineral information extraction due to high accuracy and clear object features. By reviewing these applications, we can better understand the results of previous studies and more effectively use the latest data and methods to efficiently extract key geological information. Compared with optical satellite images, synthetic-aperture radar (SAR) images are stereoscopic and exhibit clear relief, strong performance, and good detection of terrain, landforms, and other information. SAR images have been applied to seismic mechanism research, volcanic monitoring, topographic deformation, and fault analysis. Furthermore, a multi-standard maturity analysis of the geological applications of HR images reveals that optical remote sensing data are superior to radar data for mining, geological disaster, lithologic, and volcanic applications, but inferior for earthquake, glacial, and fault applications. Therefore, it is necessary for geological remote sensing research to be truly multi-disciplinary or inter-disciplinary, ensuring more detailed and efficient surveys through cross-linking with other disciplines. Moreover, the recent application of deep learning technology to remote sensing data extraction has improved the capabilities of automatic processing and data analysis with HR images.

show abstract

The Feature Selection and Extraction of Hyperspectral Mineralization Information Based on Rough Sets Theory

Cited by 3 publications

References 9 publications

A Review of Geological Applications of High-Spatial-Resolution Remote Sensing Data

A Review of Geological Applications of High-Spatial-Resolution Remote Sensing Data

Freeway Traffic Flow Model Based on Rough Sets and Elman Neural Network

A Review of Geological Applications of High-Spatial-Resolution Remote Sensing Data

Contact Info

Product

Resources

About