Multi-temporal RADARSAT-2 polarimetric SAR for maize mapping supported by segmentations from high-resolution optical image |
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| Affiliation: | 1. Department of Earth and Environmental Sciences, Michigan State University, East Lansing MI 48824, United States;2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;3. Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;4. Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;5. Beijing Polytechnic College, Beijing 100042, China;1. Department of Environmental Resources Engineering, State University of New York College of Environmental Science and Forestry, 1 Forestry Dr., Syracuse, NY 13210, USA;2. Department of Forest and Natural Resources Management, State University of New York College of Environmental Science and Forestry, 1 Forestry Dr., Syracuse, NY 13210, USA;1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China;2. Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK;1. Deptartment of Geography, Environment, and Geomatics, University of Ottawa, Canada;2. Ottawa Center of Research & Development, Agriculture & Agri-Food, Canada;3. Science and Technology Branch, Agriculture and Agri-Food Canada, Winnipeg, Canada;4. Department of Geography and Environmental Studies, University of Ottawa, Ottawa, Canada;1. Department of Geography, York University, Toronto, Ontario M3J 1P3, Canada;2. GeoConnections & Canadian Geo-Secretariat, Natural Resources Canada, Ottawa, Ontario K1A 0E9, Canada;3. Canada Center for Remote Sensing, Ottawa, Ontario K1A 0Y7, Canada |
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| Abstract: | Due to its ability to penetrate the cloud, Synthetic Aperture Radar (SAR) has been a great resource for crop mapping. Previous research has verified the applicability of SAR imagery in object-oriented crop classification, however, speckle noise limits the generation of optimal segmentation. This paper proposed an innovative SAR-based maize mapping method supported by optical image, Gaofen-1 PMS, based segmentation, named as parcel-based SAR classification assisted by optical imagery-based segmentation (os-PSC). Polarimetric decomposition was applied to extract polarimetric parameters from multi-temporal RADARSAT-2 data. One Gaofen-1 image was then used for parcel extraction, which was the basic unit for SAR image analysis. The final step was a multi-step classification for final maize mapping including: the potential maize mask extraction, pure/mixed maize parcel division and an integrated maize map production. Results showed that the overall accuracy of the os-PSC method was 89.1%, higher than those of pixel-level classification and SAR-based segmentation methods. The comparison between optical- and SAR-based segmentation demonstrated that optical-based segmentation would be better at representing maize field boundaries than the SAR-based segmentation. Moreover, the parcel- and pixel-level integrated classification will be suitable for many agricultural systems with small landownership where inter-cropping is common. Through integrating advantages of the SAR and optical data, os-PSC shows promising potentials for crop mapping. |
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| Keywords: | Maize PolSAR imagery Optical imagery Parcel- and pixel-level integrated classification |
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