Extraction of Alteration Information from Hyperspectral Data Base on Kernel Extreme Learning Machine
Machine learning, as an increasingly prominent method in recent years, has introduced new methodologies and perspectives for extracting geological alteration information. To enhance the accuracy of remote-sensing-alteration mineral information, this study focuses on the extraction of alteration info...
Saved in:
| Main Authors: | Shuhan Yang, Shufang Tian |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-09-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/19/3646 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Hyperspectral Image Assessment of Archaeo-Paleoanthropological Stratigraphic Deposits from Atapuerca (Burgos, Spain)
by: Berta García-Fernández, et al.
Published: (2025-06-01) -
Mapping and discrimination of the mineralization potential in the Bonako area (Central Cameroon Domain): Insights from Landsat 9 OLI data, GIS fuzzy modeling techniques and field observations
by: Nguimezap Marie Madeleine, et al.
Published: (2025-02-01) -
Hyperspectral Image Reconstruction Based on Blur–Kernel–Prior and Spatial–Spectral Attention
by: Hongyu Xie, et al.
Published: (2025-04-01) -
Extraction and analysis of alteration minerals from GF-5 hyperspectral data: a case study of the quartz-vein type tungsten deposit in Jiaoxi, Tibet
by: Ziqiong Guan, et al.
Published: (2025-12-01) -
Classification of maize seed hyperspectral images based on variable-depth convolutional kernels
by: Yating Hu, et al.
Published: (2025-06-01)