Spatial Perspectives toward the Recommendation of Remote Sensing Images Using the INDEX Indicator, Based on Principal Component Analysis
Progress in the development of sensor technology has increased the speed and convenience of remote sensing (RS) image acquisition. As the volume of RS images steadily increases, the challenge is no longer in producing and acquiring an RS image, but in finding a particular image from numerous RS imag...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/8/1277 |
| _version_ | 1797570315096686592 |
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| author | Jung-Hong Hong Zeal Li-Tse Su Eric Hsueh-Chan Lu |
| author_facet | Jung-Hong Hong Zeal Li-Tse Su Eric Hsueh-Chan Lu |
| author_sort | Jung-Hong Hong |
| collection | DOAJ |
| description | Progress in the development of sensor technology has increased the speed and convenience of remote sensing (RS) image acquisition. As the volume of RS images steadily increases, the challenge is no longer in producing and acquiring an RS image, but in finding a particular image from numerous RS images that precisely meets user application needs. Some spatial measuring methods specific to the recommendation of RS images have been proposed and could be used to score and sort RS images according to users’ requests. Our previous study introduced two measuring methods, namely, available space (AS) and image extension (IE), which have similar results but complementary effects for spatially ranking recommended images. The AS indicator could cover the inadequacies of the IE indicator in some cases and vice versa. The current study combines these two indicators using principal component analysis and produced a new indicator called INDEX, which we used in the RS image spatial recommendation. The ranking results were measured using a normalized discounted cumulative gain (NDCG) and several other statistic criteria. The results indicate that users are more satisfied with the recommendations of the INDEX indicator than those of AS, IE and Hausdorff distance for single RS image type selections which is the most common scenario for RS image applications. When dealing with hybrid RS image types, the INDEX indicator performs very closely to the dominant IE indicator, yet maintaining the characteristics of the AS indicator. |
| first_indexed | 2024-03-10T20:24:11Z |
| format | Article |
| id | doaj.art-6b8993a30dfb42e096c783f6ce488d34 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:24:11Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6b8993a30dfb42e096c783f6ce488d342023-11-19T21:57:21ZengMDPI AGRemote Sensing2072-42922020-04-01128127710.3390/rs12081277Spatial Perspectives toward the Recommendation of Remote Sensing Images Using the INDEX Indicator, Based on Principal Component AnalysisJung-Hong Hong0Zeal Li-Tse Su1Eric Hsueh-Chan Lu2Department of Geomatics, National Cheng Kung University, Tainan 701, TaiwanDepartment of Geomatics, National Cheng Kung University, Tainan 701, TaiwanDepartment of Geomatics, National Cheng Kung University, Tainan 701, TaiwanProgress in the development of sensor technology has increased the speed and convenience of remote sensing (RS) image acquisition. As the volume of RS images steadily increases, the challenge is no longer in producing and acquiring an RS image, but in finding a particular image from numerous RS images that precisely meets user application needs. Some spatial measuring methods specific to the recommendation of RS images have been proposed and could be used to score and sort RS images according to users’ requests. Our previous study introduced two measuring methods, namely, available space (AS) and image extension (IE), which have similar results but complementary effects for spatially ranking recommended images. The AS indicator could cover the inadequacies of the IE indicator in some cases and vice versa. The current study combines these two indicators using principal component analysis and produced a new indicator called INDEX, which we used in the RS image spatial recommendation. The ranking results were measured using a normalized discounted cumulative gain (NDCG) and several other statistic criteria. The results indicate that users are more satisfied with the recommendations of the INDEX indicator than those of AS, IE and Hausdorff distance for single RS image type selections which is the most common scenario for RS image applications. When dealing with hybrid RS image types, the INDEX indicator performs very closely to the dominant IE indicator, yet maintaining the characteristics of the AS indicator.https://www.mdpi.com/2072-4292/12/8/1277remote sensing imagespatial sortingprincipal component analysis |
| spellingShingle | Jung-Hong Hong Zeal Li-Tse Su Eric Hsueh-Chan Lu Spatial Perspectives toward the Recommendation of Remote Sensing Images Using the INDEX Indicator, Based on Principal Component Analysis Remote Sensing remote sensing image spatial sorting principal component analysis |
| title | Spatial Perspectives toward the Recommendation of Remote Sensing Images Using the INDEX Indicator, Based on Principal Component Analysis |
| title_full | Spatial Perspectives toward the Recommendation of Remote Sensing Images Using the INDEX Indicator, Based on Principal Component Analysis |
| title_fullStr | Spatial Perspectives toward the Recommendation of Remote Sensing Images Using the INDEX Indicator, Based on Principal Component Analysis |
| title_full_unstemmed | Spatial Perspectives toward the Recommendation of Remote Sensing Images Using the INDEX Indicator, Based on Principal Component Analysis |
| title_short | Spatial Perspectives toward the Recommendation of Remote Sensing Images Using the INDEX Indicator, Based on Principal Component Analysis |
| title_sort | spatial perspectives toward the recommendation of remote sensing images using the index indicator based on principal component analysis |
| topic | remote sensing image spatial sorting principal component analysis |
| url | https://www.mdpi.com/2072-4292/12/8/1277 |
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