Automating band selection for hyperspectral indices: bridging AVIRIS-NG and Sentinel-2 satellite data for earth science applications
Hyperspectral data from the Airborne Visible and Infra-Red Imaging Spectrometer – Next-Generation (AVIRIS-NG) offers transformative potential for Earth science research, enabling detailed analysis of land surface processes, resource monitoring, and environmental dynamics. This study presents an auto...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2024.1487160/full |
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| author | Veerendra Satya Sylesh Peddinti Venkata Ravibabu Mandla Shashi Mesapam Suresh Kancharla |
| author_facet | Veerendra Satya Sylesh Peddinti Venkata Ravibabu Mandla Shashi Mesapam Suresh Kancharla |
| author_sort | Veerendra Satya Sylesh Peddinti |
| collection | DOAJ |
| description | Hyperspectral data from the Airborne Visible and Infra-Red Imaging Spectrometer – Next-Generation (AVIRIS-NG) offers transformative potential for Earth science research, enabling detailed analysis of land surface processes, resource monitoring, and environmental dynamics. This study presents an automated methodology to optimize the selection of AVIRIS spectral bands, improving the computation of indices critical to Earth science applications. By leveraging multiple hyperspectral bands, the approach enhances the accuracy of indices used to monitor water resources, vegetation health, urban expansion, and built-up areas. The methodology involves calculating indices from all possible AVIRIS band combinations, evaluating their root mean squared error (RMSE) against Sentinel-2 indices, reducing RMSE skewness, and selecting bands with minimal deviation for specific Land Use Land Cover (LULC) categories. The process is automated and employs parallel processing with Python, significantly reducing execution time and enabling scalability for large geospatial datasets. Key indices, including the Normalized Difference Water Index (NDWI), Normalized Difference Red Edge (NDRE), and Normalized Difference Built-up Index (NDBI), Green Normalized Difference Vegetation Index (GNDVI) were validated using the proposed methodology. Results demonstrate the potential of hyperspectral data to outperform traditional single-band approaches, providing more precise and reliable assessments. |
| format | Article |
| id | doaj-art-ec92c6fa0cef462e8a2cf5a9df82e86b |
| institution | Kabale University |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj-art-ec92c6fa0cef462e8a2cf5a9df82e86b2025-01-27T06:40:56ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-01-011210.3389/feart.2024.14871601487160Automating band selection for hyperspectral indices: bridging AVIRIS-NG and Sentinel-2 satellite data for earth science applicationsVeerendra Satya Sylesh Peddinti0Venkata Ravibabu Mandla1Shashi Mesapam2Suresh Kancharla3Department of Civil Engineering, National Institute of Technology (NIT), Warangal, IndiaCentre for Information and Communication Technology (CICT), National Institute of rural Development and Panchayat Raj (NIRDPR), Ministry of Rural Development, Hyderabad, IndiaDepartment of Civil Engineering, National Institute of Technology (NIT), Warangal, IndiaIndian Council of Agricultural Research – IIOPR, Pedavegi, IndiaHyperspectral data from the Airborne Visible and Infra-Red Imaging Spectrometer – Next-Generation (AVIRIS-NG) offers transformative potential for Earth science research, enabling detailed analysis of land surface processes, resource monitoring, and environmental dynamics. This study presents an automated methodology to optimize the selection of AVIRIS spectral bands, improving the computation of indices critical to Earth science applications. By leveraging multiple hyperspectral bands, the approach enhances the accuracy of indices used to monitor water resources, vegetation health, urban expansion, and built-up areas. The methodology involves calculating indices from all possible AVIRIS band combinations, evaluating their root mean squared error (RMSE) against Sentinel-2 indices, reducing RMSE skewness, and selecting bands with minimal deviation for specific Land Use Land Cover (LULC) categories. The process is automated and employs parallel processing with Python, significantly reducing execution time and enabling scalability for large geospatial datasets. Key indices, including the Normalized Difference Water Index (NDWI), Normalized Difference Red Edge (NDRE), and Normalized Difference Built-up Index (NDBI), Green Normalized Difference Vegetation Index (GNDVI) were validated using the proposed methodology. Results demonstrate the potential of hyperspectral data to outperform traditional single-band approaches, providing more precise and reliable assessments.https://www.frontiersin.org/articles/10.3389/feart.2024.1487160/fullAVIRISautomationband selectionhyperspectral dataindicesparallel processing |
| spellingShingle | Veerendra Satya Sylesh Peddinti Venkata Ravibabu Mandla Shashi Mesapam Suresh Kancharla Automating band selection for hyperspectral indices: bridging AVIRIS-NG and Sentinel-2 satellite data for earth science applications Frontiers in Earth Science AVIRIS automation band selection hyperspectral data indices parallel processing |
| title | Automating band selection for hyperspectral indices: bridging AVIRIS-NG and Sentinel-2 satellite data for earth science applications |
| title_full | Automating band selection for hyperspectral indices: bridging AVIRIS-NG and Sentinel-2 satellite data for earth science applications |
| title_fullStr | Automating band selection for hyperspectral indices: bridging AVIRIS-NG and Sentinel-2 satellite data for earth science applications |
| title_full_unstemmed | Automating band selection for hyperspectral indices: bridging AVIRIS-NG and Sentinel-2 satellite data for earth science applications |
| title_short | Automating band selection for hyperspectral indices: bridging AVIRIS-NG and Sentinel-2 satellite data for earth science applications |
| title_sort | automating band selection for hyperspectral indices bridging aviris ng and sentinel 2 satellite data for earth science applications |
| topic | AVIRIS automation band selection hyperspectral data indices parallel processing |
| url | https://www.frontiersin.org/articles/10.3389/feart.2024.1487160/full |
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