A highly efficient temporal-spatial probability synthesized model from multi-temporal remote sensing for paddy rice identification
This article develops a temporal-spatial probability synthesized model (TSPSM), in which a metric describing the characteristic of temporal and spatial information is defined to map paddy rice distribution. The purpose is to reduce the effect of cloud contamination on classification. The error matri...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2017-01-01
|
| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/22797254.2017.1279819 |
| _version_ | 1818351685269979136 |
|---|---|
| author | Peijun Sun Jinshui Zhang Xiufang Zhu Yaozhong Pan Hongli Liu |
| author_facet | Peijun Sun Jinshui Zhang Xiufang Zhu Yaozhong Pan Hongli Liu |
| author_sort | Peijun Sun |
| collection | DOAJ |
| description | This article develops a temporal-spatial probability synthesized model (TSPSM), in which a metric describing the characteristic of temporal and spatial information is defined to map paddy rice distribution. The purpose is to reduce the effect of cloud contamination on classification. The error matrix and Kappa were used as accuracy measurement. Results showed that TSPSM obtained higher accuracy with significant difference from error matrices of the other two conventional methods, post comparison classification with post-classification comparison and majority voting. Moreover, smaller window was suitable for the area with higher fragmentation, while the larger was suitable for the area with lower fragmentation. It was concluded that TSPSM could help to improve the potentials of temporal optical image to map crops. |
| first_indexed | 2024-12-13T18:41:40Z |
| format | Article |
| id | doaj.art-8789b325d58e4698842fcf3d34061124 |
| institution | Directory Open Access Journal |
| issn | 2279-7254 |
| language | English |
| last_indexed | 2024-12-13T18:41:40Z |
| publishDate | 2017-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj.art-8789b325d58e4698842fcf3d340611242022-12-21T23:35:12ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542017-01-015019811010.1080/22797254.2017.12798191279819A highly efficient temporal-spatial probability synthesized model from multi-temporal remote sensing for paddy rice identificationPeijun Sun0Jinshui Zhang1Xiufang Zhu2Yaozhong Pan3Hongli Liu4Beijing Normal University, State Key Laboratory of Earth Surface Processes and Resource EcologyBeijing Normal University, State Key Laboratory of Earth Surface Processes and Resource EcologyBeijing Normal University, State Key Laboratory of Earth Surface Processes and Resource EcologyBeijing Normal University, State Key Laboratory of Earth Surface Processes and Resource EcologyBeijing Normal University, State Key Laboratory of Earth Surface Processes and Resource EcologyThis article develops a temporal-spatial probability synthesized model (TSPSM), in which a metric describing the characteristic of temporal and spatial information is defined to map paddy rice distribution. The purpose is to reduce the effect of cloud contamination on classification. The error matrix and Kappa were used as accuracy measurement. Results showed that TSPSM obtained higher accuracy with significant difference from error matrices of the other two conventional methods, post comparison classification with post-classification comparison and majority voting. Moreover, smaller window was suitable for the area with higher fragmentation, while the larger was suitable for the area with lower fragmentation. It was concluded that TSPSM could help to improve the potentials of temporal optical image to map crops.http://dx.doi.org/10.1080/22797254.2017.1279819Paddy ricetemporal-spatial probabilitylandscape featuresLandsat 8 OLI |
| spellingShingle | Peijun Sun Jinshui Zhang Xiufang Zhu Yaozhong Pan Hongli Liu A highly efficient temporal-spatial probability synthesized model from multi-temporal remote sensing for paddy rice identification European Journal of Remote Sensing Paddy rice temporal-spatial probability landscape features Landsat 8 OLI |
| title | A highly efficient temporal-spatial probability synthesized model from multi-temporal remote sensing for paddy rice identification |
| title_full | A highly efficient temporal-spatial probability synthesized model from multi-temporal remote sensing for paddy rice identification |
| title_fullStr | A highly efficient temporal-spatial probability synthesized model from multi-temporal remote sensing for paddy rice identification |
| title_full_unstemmed | A highly efficient temporal-spatial probability synthesized model from multi-temporal remote sensing for paddy rice identification |
| title_short | A highly efficient temporal-spatial probability synthesized model from multi-temporal remote sensing for paddy rice identification |
| title_sort | highly efficient temporal spatial probability synthesized model from multi temporal remote sensing for paddy rice identification |
| topic | Paddy rice temporal-spatial probability landscape features Landsat 8 OLI |
| url | http://dx.doi.org/10.1080/22797254.2017.1279819 |
| work_keys_str_mv | AT peijunsun ahighlyefficienttemporalspatialprobabilitysynthesizedmodelfrommultitemporalremotesensingforpaddyriceidentification AT jinshuizhang ahighlyefficienttemporalspatialprobabilitysynthesizedmodelfrommultitemporalremotesensingforpaddyriceidentification AT xiufangzhu ahighlyefficienttemporalspatialprobabilitysynthesizedmodelfrommultitemporalremotesensingforpaddyriceidentification AT yaozhongpan ahighlyefficienttemporalspatialprobabilitysynthesizedmodelfrommultitemporalremotesensingforpaddyriceidentification AT hongliliu ahighlyefficienttemporalspatialprobabilitysynthesizedmodelfrommultitemporalremotesensingforpaddyriceidentification AT peijunsun highlyefficienttemporalspatialprobabilitysynthesizedmodelfrommultitemporalremotesensingforpaddyriceidentification AT jinshuizhang highlyefficienttemporalspatialprobabilitysynthesizedmodelfrommultitemporalremotesensingforpaddyriceidentification AT xiufangzhu highlyefficienttemporalspatialprobabilitysynthesizedmodelfrommultitemporalremotesensingforpaddyriceidentification AT yaozhongpan highlyefficienttemporalspatialprobabilitysynthesizedmodelfrommultitemporalremotesensingforpaddyriceidentification AT hongliliu highlyefficienttemporalspatialprobabilitysynthesizedmodelfrommultitemporalremotesensingforpaddyriceidentification |