Rice cropping information extraction mapping based on sample knowledge mining using high resolution remote sensing images
The rice fields in high resolution remote sensing images present mixed information constituted by distinct ground objects such as rice, soil, water, weed, duckweed and so on. Thus a novel approach for mapping of rice cropping areas based on sample knowledge mining was brought up according to spatial...
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| Format: | Article |
| Language: | English |
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Zhejiang University Press
2018-11-01
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| Series: | 浙江大学学报. 农业与生命科学版 |
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| Online Access: | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2017.11.101 |
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| author | YIN Huafeng SU Cheng FENG Cunjun LI Yuqin HUANG Zhicai ZHANG Xiaocan |
| author_facet | YIN Huafeng SU Cheng FENG Cunjun LI Yuqin HUANG Zhicai ZHANG Xiaocan |
| author_sort | YIN Huafeng |
| collection | DOAJ |
| description | The rice fields in high resolution remote sensing images present mixed information constituted by distinct ground objects such as rice, soil, water, weed, duckweed and so on. Thus a novel approach for mapping of rice cropping areas based on sample knowledge mining was brought up according to spatial autocorrelation theory, which took advantage of the spectra combinational regularity. The accompanying mapping strategy was formulated based on this method. First, we segmented the high resolution remote sensing image into spectrally homogeneous base-units that represented distinct mixture information of several ground objects by grouping adjacent pixels with similar spectra. Second, we constructed a set of rice base-unit types through analysis of the base-unit types that contain rice field samples, and combine all the base-unit whose type belonged to this set to form initial rice cropping region. Finally, we vectorized the initial rice cropping region to initial rice cropping polygons, and then removed the polygons incompatible with spectra combinational regularity of rice fields through similarity analysis of combined feature of base-units between the rice cropping polygons and the rice field sample polygons. The overall accuracy of experimental rice cropping areas mapping results was over 96%. The successful application of this novel approach proves its efficiency and indicates its great potential for further utilization. |
| format | Article |
| id | doaj-art-1b7170becef048d8a23c4f5789fadee7 |
| institution | Matheson Library |
| issn | 1008-9209 2097-5155 |
| language | English |
| publishDate | 2018-11-01 |
| publisher | Zhejiang University Press |
| record_format | Article |
| series | 浙江大学学报. 农业与生命科学版 |
| spelling | doaj-art-1b7170becef048d8a23c4f5789fadee72025-08-01T05:30:16ZengZhejiang University Press浙江大学学报. 农业与生命科学版1008-92092097-51552018-11-014476577410.3785/j.issn.1008-9209.2017.11.10110089209Rice cropping information extraction mapping based on sample knowledge mining using high resolution remote sensing imagesYIN HuafengSU ChengFENG CunjunLI YuqinHUANG ZhicaiZHANG XiaocanThe rice fields in high resolution remote sensing images present mixed information constituted by distinct ground objects such as rice, soil, water, weed, duckweed and so on. Thus a novel approach for mapping of rice cropping areas based on sample knowledge mining was brought up according to spatial autocorrelation theory, which took advantage of the spectra combinational regularity. The accompanying mapping strategy was formulated based on this method. First, we segmented the high resolution remote sensing image into spectrally homogeneous base-units that represented distinct mixture information of several ground objects by grouping adjacent pixels with similar spectra. Second, we constructed a set of rice base-unit types through analysis of the base-unit types that contain rice field samples, and combine all the base-unit whose type belonged to this set to form initial rice cropping region. Finally, we vectorized the initial rice cropping region to initial rice cropping polygons, and then removed the polygons incompatible with spectra combinational regularity of rice fields through similarity analysis of combined feature of base-units between the rice cropping polygons and the rice field sample polygons. The overall accuracy of experimental rice cropping areas mapping results was over 96%. The successful application of this novel approach proves its efficiency and indicates its great potential for further utilization.https://www.academax.com/doi/10.3785/j.issn.1008-9209.2017.11.101rice cropping information extractionhigh resolution remote sensing imagessample knowledge miningspatial autocorrelation |
| spellingShingle | YIN Huafeng SU Cheng FENG Cunjun LI Yuqin HUANG Zhicai ZHANG Xiaocan Rice cropping information extraction mapping based on sample knowledge mining using high resolution remote sensing images 浙江大学学报. 农业与生命科学版 rice cropping information extraction high resolution remote sensing images sample knowledge mining spatial autocorrelation |
| title | Rice cropping information extraction mapping based on sample knowledge mining using high resolution remote sensing images |
| title_full | Rice cropping information extraction mapping based on sample knowledge mining using high resolution remote sensing images |
| title_fullStr | Rice cropping information extraction mapping based on sample knowledge mining using high resolution remote sensing images |
| title_full_unstemmed | Rice cropping information extraction mapping based on sample knowledge mining using high resolution remote sensing images |
| title_short | Rice cropping information extraction mapping based on sample knowledge mining using high resolution remote sensing images |
| title_sort | rice cropping information extraction mapping based on sample knowledge mining using high resolution remote sensing images |
| topic | rice cropping information extraction high resolution remote sensing images sample knowledge mining spatial autocorrelation |
| url | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2017.11.101 |
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