Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A Review
According to existing signatures for various kinds of land cover coming from different spectral bands, i.e., optical, thermal infrared and PolSAR, it is possible to infer about the land cover type having a single decision from each of the spectral bands. Fusing these decisions, it is possible to rad...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/10/1/15 |
| _version_ | 1797343339954044928 |
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| author | Spiros Papadopoulos Georgia Koukiou Vassilis Anastassopoulos |
| author_facet | Spiros Papadopoulos Georgia Koukiou Vassilis Anastassopoulos |
| author_sort | Spiros Papadopoulos |
| collection | DOAJ |
| description | According to existing signatures for various kinds of land cover coming from different spectral bands, i.e., optical, thermal infrared and PolSAR, it is possible to infer about the land cover type having a single decision from each of the spectral bands. Fusing these decisions, it is possible to radically improve the reliability of the decision regarding each pixel, taking into consideration the correlation of the individual decisions of the specific pixel as well as additional information transferred from the pixels’ neighborhood. Different remotely sensed data contribute their own information regarding the characteristics of the materials lying in each separate pixel. Hyperspectral and multispectral images give analytic information regarding the reflectance of each pixel in a very detailed manner. Thermal infrared images give valuable information regarding the temperature of the surface covered by each pixel, which is very important for recording thermal locations in urban regions. Finally, SAR data provide structural and electrical characteristics of each pixel. Combining information from some of these sources further improves the capability for reliable categorization of each pixel. The necessary mathematical background regarding pixel-based classification and decision fusion methods is analytically presented. |
| first_indexed | 2024-03-08T10:46:11Z |
| format | Article |
| id | doaj.art-55fdd3ff9ff446f7ad6186f34b99ac57 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-08T10:46:11Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-55fdd3ff9ff446f7ad6186f34b99ac572024-01-26T17:11:02ZengMDPI AGJournal of Imaging2313-433X2024-01-011011510.3390/jimaging10010015Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A ReviewSpiros Papadopoulos0Georgia Koukiou1Vassilis Anastassopoulos2Electronics Laboratory, Physics Department, University of Patras, 26504 Patras, GreeceElectronics Laboratory, Physics Department, University of Patras, 26504 Patras, GreeceElectronics Laboratory, Physics Department, University of Patras, 26504 Patras, GreeceAccording to existing signatures for various kinds of land cover coming from different spectral bands, i.e., optical, thermal infrared and PolSAR, it is possible to infer about the land cover type having a single decision from each of the spectral bands. Fusing these decisions, it is possible to radically improve the reliability of the decision regarding each pixel, taking into consideration the correlation of the individual decisions of the specific pixel as well as additional information transferred from the pixels’ neighborhood. Different remotely sensed data contribute their own information regarding the characteristics of the materials lying in each separate pixel. Hyperspectral and multispectral images give analytic information regarding the reflectance of each pixel in a very detailed manner. Thermal infrared images give valuable information regarding the temperature of the surface covered by each pixel, which is very important for recording thermal locations in urban regions. Finally, SAR data provide structural and electrical characteristics of each pixel. Combining information from some of these sources further improves the capability for reliable categorization of each pixel. The necessary mathematical background regarding pixel-based classification and decision fusion methods is analytically presented.https://www.mdpi.com/2313-433X/10/1/15land cover classificationdecision fusionpixel-levelmulti-band datathermal imagesSAR images |
| spellingShingle | Spiros Papadopoulos Georgia Koukiou Vassilis Anastassopoulos Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A Review Journal of Imaging land cover classification decision fusion pixel-level multi-band data thermal images SAR images |
| title | Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A Review |
| title_full | Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A Review |
| title_fullStr | Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A Review |
| title_full_unstemmed | Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A Review |
| title_short | Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A Review |
| title_sort | decision fusion at pixel level of multi band data for land cover classification a review |
| topic | land cover classification decision fusion pixel-level multi-band data thermal images SAR images |
| url | https://www.mdpi.com/2313-433X/10/1/15 |
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