Sub-pixel mineral mapping using EO-1 Hyperion hyperspectral data

18 citations as recorded by crossref.

1. Automated lithological mapping by integrating spectral enhancement techniques and machine learning algorithms using AVIRIS-NG hyperspectral data in Gold-bearing granite-greenstone rocks in Hutti, India C. Kumar et al. https://doi.org/10.1016/j.jag.2019.102006
2. Mapping hydrothermal alteration minerals using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski gold deposit area, India C. Kumar et al. https://doi.org/10.1080/01431161.2019.1648906
3. Geological and Mineralogical Mapping Based on Statistical Methods of Remote Sensing Data Processing of Landsat-8: A Case Study in the Southeastern Transbaikalia, Russia I. Nafigin et al. https://doi.org/10.3390/su14159242
4. Impact of Image-Processing Routines on Mapping Glacier Surface Facies from Svalbard and the Himalayas Using Pixel-Based Methods S. Jawak et al. https://doi.org/10.3390/rs14061414
5. Spectral pathways for effective delineation of mafic-ultramafic rocks by using PRISMA hyperspectral data N. Libeesh & S. Arivazhagan https://doi.org/10.1016/j.rsase.2023.100955
6. EXhype: A tool for mineral classification using hyperspectral data R. Adep et al. https://doi.org/10.1016/j.isprsjprs.2016.12.012
7. Satellite image derived spectral modeling to assess the grades of hematite deposits: a study on Noamundi area in West Singhbhum district, Jharkhand S. Panda et al. https://doi.org/10.1080/10106049.2019.1594395
8. AVIRIS-NG hyperspectral data analysis for pre- and post-MNF transformation using per-pixel classification algorithms L. Sharma & R. Verma https://doi.org/10.1080/10106049.2020.1801857
9. Integrating Data of ASTER and Landsat-8 OLI (AO) for Hydrothermal Alteration Mineral Mapping in Duolong Porphyry Cu-Au Deposit, Tibetan Plateau, China T. Zhang et al. https://doi.org/10.3390/rs8110890
10. Integrated Geophysical and Remote Sensing Analysis for Identifying Mineralization Potential in Wadi Queih, Central Eastern Desert, Egypt A. Sameer et al. https://doi.org/10.3390/resources15090116
11. Identification and mapping of high-potential iron ore alteration zone across Joda, Odisha using ASTER and EO-1 hyperion data A. Sengupta et al. https://doi.org/10.1080/14498596.2018.1485120
12. Design and Development of Higher Order Spectral Unmixing Model for Mangrove Species Discrimination S. Chakravortty & S. Chakrabarti https://doi.org/10.1007/s40010-017-0434-x
13. New effective spectral matching measures for hyperspectral data analysis C. Kumar et al. https://doi.org/10.1080/01431161.2021.1890265
14. Exploring Porphyry Copper Deposits in the Central Iran Using Remote Sensing Techniques A. Mahan & R. Arfania https://doi.org/10.4236/ojg.2018.86035
15. Employing both full and partial sub-pixel mapping methods to delineate hydrothermal alteration zones associated with porphyry copper deposits Y. Bahrami et al. https://doi.org/10.1016/j.ejrs.2025.05.007
16. Pixel and Object-Based Machine Learning Classification Schemes for Lithological Mapping Enhancement of Semi-Arid Regions Using Sentinel-2A Imagery: A Case Study of the Southern Moroccan Meseta I. Serbouti et al. https://doi.org/10.1109/ACCESS.2021.3107294
17. Spectral characteristics of talc and mineral abundance mapping in the Jahazpur Belt of Rajasthan, India using AVIRIS-NG data B. Bhadra et al. https://doi.org/10.1080/01431161.2020.1783710
18. Mapping of mineral resources and lithological units: a review of remote sensing techniques R. Rajan Girija & S. Mayappan https://doi.org/10.1080/19479832.2019.1589585