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...
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MDPI AG
2024-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/19/3646 |
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| author | Shuhan Yang Shufang Tian |
| author_facet | Shuhan Yang Shufang Tian |
| author_sort | Shuhan Yang |
| collection | DOAJ |
| description | 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 information from hyperspectral remote sensing data using the Kernel-Based Extreme Learning Machine (KELM) optimized with the Sparrow Search Algorithm (SSA). The ideal parameters of the Kernel Extreme Learning Machine model were successfully acquired by utilizing the sparrow optimization method for continuous search and iteration, avoiding the blindness and arbitrariness associated with parameter selection by humans. Spectral Angle Mapper (SAM) technology was used to extract sample data from hyperspectral imagery, which were then used to train the machine learning model for alteration information extraction. The experimental results show that, when compared to the Random Forest and the Support Vector Machine algorithms, the Kernel-Based Extreme Learning Machine algorithm achieved the highest accuracy and the best effect in the extraction results. It closely matches the known mineral points and geochemical anomalies in the area, confirming that the method has a clear advantage in the extraction of hyperspectral alteration information. |
| format | Article |
| id | doaj-art-f4c49f5049c0483eb6f1a8e3d6b55c1c |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-f4c49f5049c0483eb6f1a8e3d6b55c1c2025-08-20T01:47:36ZengMDPI AGRemote Sensing2072-42922024-09-011619364610.3390/rs16193646Extraction of Alteration Information from Hyperspectral Data Base on Kernel Extreme Learning MachineShuhan Yang0Shufang Tian1School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, ChinaSchool of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, ChinaMachine 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 information from hyperspectral remote sensing data using the Kernel-Based Extreme Learning Machine (KELM) optimized with the Sparrow Search Algorithm (SSA). The ideal parameters of the Kernel Extreme Learning Machine model were successfully acquired by utilizing the sparrow optimization method for continuous search and iteration, avoiding the blindness and arbitrariness associated with parameter selection by humans. Spectral Angle Mapper (SAM) technology was used to extract sample data from hyperspectral imagery, which were then used to train the machine learning model for alteration information extraction. The experimental results show that, when compared to the Random Forest and the Support Vector Machine algorithms, the Kernel-Based Extreme Learning Machine algorithm achieved the highest accuracy and the best effect in the extraction results. It closely matches the known mineral points and geochemical anomalies in the area, confirming that the method has a clear advantage in the extraction of hyperspectral alteration information.https://www.mdpi.com/2072-4292/16/19/3646spectral angle mapperkernel-based extreme learning machinealteration information extractionhyperspectral |
| spellingShingle | Shuhan Yang Shufang Tian Extraction of Alteration Information from Hyperspectral Data Base on Kernel Extreme Learning Machine Remote Sensing spectral angle mapper kernel-based extreme learning machine alteration information extraction hyperspectral |
| title | Extraction of Alteration Information from Hyperspectral Data Base on Kernel Extreme Learning Machine |
| title_full | Extraction of Alteration Information from Hyperspectral Data Base on Kernel Extreme Learning Machine |
| title_fullStr | Extraction of Alteration Information from Hyperspectral Data Base on Kernel Extreme Learning Machine |
| title_full_unstemmed | Extraction of Alteration Information from Hyperspectral Data Base on Kernel Extreme Learning Machine |
| title_short | Extraction of Alteration Information from Hyperspectral Data Base on Kernel Extreme Learning Machine |
| title_sort | extraction of alteration information from hyperspectral data base on kernel extreme learning machine |
| topic | spectral angle mapper kernel-based extreme learning machine alteration information extraction hyperspectral |
| url | https://www.mdpi.com/2072-4292/16/19/3646 |
| work_keys_str_mv | AT shuhanyang extractionofalterationinformationfromhyperspectraldatabaseonkernelextremelearningmachine AT shufangtian extractionofalterationinformationfromhyperspectraldatabaseonkernelextremelearningmachine |