Application of Inverse Mapping for Automated Determination of Normalized Indices Useful for Land Surface Classification
Precise surface classification is essential for glacial health monitoring, where normalized indices have traditionally been used. These indices are created empirically for a specific sensor. The transferability of these indices to other sensors can be affected by differences in spectral and spatial...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10227536/ |
| _version_ | 1797685397274230784 |
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| author | Gunjan Joshi Ryo Natsuaki Akira Hirose |
| author_facet | Gunjan Joshi Ryo Natsuaki Akira Hirose |
| author_sort | Gunjan Joshi |
| collection | DOAJ |
| description | Precise surface classification is essential for glacial health monitoring, where normalized indices have traditionally been used. These indices are created empirically for a specific sensor. The transferability of these indices to other sensors can be affected by differences in spectral and spatial resolution. Thus, it is essential to evaluate the transferability of an index before applying it to a new sensor to ensure accuracy and reliability. However, as the number of satellites, sensors, and observation bands increases, there is a need for automated methods for determining application-specific normalized indices. In this article, we propose using all the bands of multispectral optical sensors to generate multiple normalized indices and determining application-specific indices using inverse mapping. We use these normalized indices for pixel-by-pixel surface classification using neural networks. First, we employ all the bands for generating normalized indices and then eliminate low-spatial-resolution bands to evaluate classification performance by using only high-spatial-resolution indices. We apply this method to a glacial region and observe 81.98% and 84.81% overall accuracy compared to the ground truth data for the two classifications, respectively. We then apply inverse mapping dynamics to the classification results to determine prominent indices useful for glacier classification. The results show that although some of the determined indices are not traditional indices, they are still useful for classification due to the relative differences between various land types. The proposed method has the potential to automate normalized index determination, thereby eliminating the need for empirical band assessment methods and making the index development process more efficient. |
| first_indexed | 2024-03-12T00:44:35Z |
| format | Article |
| id | doaj.art-d5612dc18c6548049f1422cccebc4c50 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-12T00:44:35Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-d5612dc18c6548049f1422cccebc4c502023-09-14T23:00:15ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01167804781810.1109/JSTARS.2023.330804910227536Application of Inverse Mapping for Automated Determination of Normalized Indices Useful for Land Surface ClassificationGunjan Joshi0https://orcid.org/0000-0001-6092-1408Ryo Natsuaki1https://orcid.org/0000-0003-2291-4375Akira Hirose2https://orcid.org/0000-0002-6936-9733Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, JapanDepartment of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, JapanDepartment of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, JapanPrecise surface classification is essential for glacial health monitoring, where normalized indices have traditionally been used. These indices are created empirically for a specific sensor. The transferability of these indices to other sensors can be affected by differences in spectral and spatial resolution. Thus, it is essential to evaluate the transferability of an index before applying it to a new sensor to ensure accuracy and reliability. However, as the number of satellites, sensors, and observation bands increases, there is a need for automated methods for determining application-specific normalized indices. In this article, we propose using all the bands of multispectral optical sensors to generate multiple normalized indices and determining application-specific indices using inverse mapping. We use these normalized indices for pixel-by-pixel surface classification using neural networks. First, we employ all the bands for generating normalized indices and then eliminate low-spatial-resolution bands to evaluate classification performance by using only high-spatial-resolution indices. We apply this method to a glacial region and observe 81.98% and 84.81% overall accuracy compared to the ground truth data for the two classifications, respectively. We then apply inverse mapping dynamics to the classification results to determine prominent indices useful for glacier classification. The results show that although some of the determined indices are not traditional indices, they are still useful for classification due to the relative differences between various land types. The proposed method has the potential to automate normalized index determination, thereby eliminating the need for empirical band assessment methods and making the index development process more efficient.https://ieeexplore.ieee.org/document/10227536/Explainable neural networkglacier surface classificationinverse mappingmultispectral imageryneural networknormalized index |
| spellingShingle | Gunjan Joshi Ryo Natsuaki Akira Hirose Application of Inverse Mapping for Automated Determination of Normalized Indices Useful for Land Surface Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Explainable neural network glacier surface classification inverse mapping multispectral imagery neural network normalized index |
| title | Application of Inverse Mapping for Automated Determination of Normalized Indices Useful for Land Surface Classification |
| title_full | Application of Inverse Mapping for Automated Determination of Normalized Indices Useful for Land Surface Classification |
| title_fullStr | Application of Inverse Mapping for Automated Determination of Normalized Indices Useful for Land Surface Classification |
| title_full_unstemmed | Application of Inverse Mapping for Automated Determination of Normalized Indices Useful for Land Surface Classification |
| title_short | Application of Inverse Mapping for Automated Determination of Normalized Indices Useful for Land Surface Classification |
| title_sort | application of inverse mapping for automated determination of normalized indices useful for land surface classification |
| topic | Explainable neural network glacier surface classification inverse mapping multispectral imagery neural network normalized index |
| url | https://ieeexplore.ieee.org/document/10227536/ |
| work_keys_str_mv | AT gunjanjoshi applicationofinversemappingforautomateddeterminationofnormalizedindicesusefulforlandsurfaceclassification AT ryonatsuaki applicationofinversemappingforautomateddeterminationofnormalizedindicesusefulforlandsurfaceclassification AT akirahirose applicationofinversemappingforautomateddeterminationofnormalizedindicesusefulforlandsurfaceclassification |