High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images
High-resolution crop mapping is of great significance in agricultural monitoring, precision agriculture, and providing critical information for crop yield or disaster monitoring. Meanwhile, medium resolution time-series optical and synthetic aperture radar (SAR) images can provide useful phenologica...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/6/1148 |
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| author | Lingbo Yang Limin Wang Ghali Abdullahi Abubakar Jingfeng Huang |
| author_facet | Lingbo Yang Limin Wang Ghali Abdullahi Abubakar Jingfeng Huang |
| author_sort | Lingbo Yang |
| collection | DOAJ |
| description | High-resolution crop mapping is of great significance in agricultural monitoring, precision agriculture, and providing critical information for crop yield or disaster monitoring. Meanwhile, medium resolution time-series optical and synthetic aperture radar (SAR) images can provide useful phenological information. Combining high-resolution satellite data and medium resolution time-series images provides a great opportunity for fine crop mapping. Simple Non-Iterative Clustering (SNIC) is a state-of-the-art image segmentation algorithm that shows the advantages of efficiency and high accuracy. However, the application of SNIC in crop mapping based on the combination of high-resolution and medium-resolution images is unknown. Besides, there is still little research on the influence of the superpixel size (one of the key user-defined parameters of the SNIC method) on classification accuracy. In this study, we employed a 2 m high-resolution GF-1 pan-sharpened image and 10 m medium resolution time-series Sentinel-1 C-band Synthetic Aperture Radar Instrument (C-SAR) and Sentinel-2 Multispectral Instrument (MSI) images to carry out rice mapping based on the SNIC method. The results show that with the increase of the superpixel size, the classification accuracy increased at first and then decreased rapidly after reaching the summit when the superpixel size is 27. The classification accuracy of the combined use of optical and SAR data is higher than that using only Sentinel-2 MSI or Sentinel-1 C-SAR vertical transmitted and vertical received (VV) or vertical transmitted and horizontal received (VH) data, with overall accuracies of 0.8335, 0.8282, 0.7862, and 0.7886, respectively. Meanwhile, the results also indicate that classification based on superpixels obtained by SNIC significantly outperforms classification based on original pixels. The overall accuracy, producer accuracy, and user accuracy of SNIC superpixel-based classification increased by 9.14%, 17.16%, 27.35% and 1.36%, respectively, when compared with the pixel-based classification, based on the combination of optical and SAR data (using the random forest as the classifier). The results show that SNIC superpixel segmentation is a feasible method for high-resolution crop mapping based on multi-source remote sensing data. The automatic selection of the optimal superpixel size of SNIC will be focused on in future research. |
| first_indexed | 2024-03-10T13:08:22Z |
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| id | doaj.art-12f5166c2cb54b2e994ccb490aac7a2f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T13:08:22Z |
| publishDate | 2021-03-01 |
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| series | Remote Sensing |
| spelling | doaj.art-12f5166c2cb54b2e994ccb490aac7a2f2023-11-21T10:53:33ZengMDPI AGRemote Sensing2072-42922021-03-01136114810.3390/rs13061148High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing ImagesLingbo Yang0Limin Wang1Ghali Abdullahi Abubakar2Jingfeng Huang3Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, ChinaInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaInstitute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, ChinaInstitute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, ChinaHigh-resolution crop mapping is of great significance in agricultural monitoring, precision agriculture, and providing critical information for crop yield or disaster monitoring. Meanwhile, medium resolution time-series optical and synthetic aperture radar (SAR) images can provide useful phenological information. Combining high-resolution satellite data and medium resolution time-series images provides a great opportunity for fine crop mapping. Simple Non-Iterative Clustering (SNIC) is a state-of-the-art image segmentation algorithm that shows the advantages of efficiency and high accuracy. However, the application of SNIC in crop mapping based on the combination of high-resolution and medium-resolution images is unknown. Besides, there is still little research on the influence of the superpixel size (one of the key user-defined parameters of the SNIC method) on classification accuracy. In this study, we employed a 2 m high-resolution GF-1 pan-sharpened image and 10 m medium resolution time-series Sentinel-1 C-band Synthetic Aperture Radar Instrument (C-SAR) and Sentinel-2 Multispectral Instrument (MSI) images to carry out rice mapping based on the SNIC method. The results show that with the increase of the superpixel size, the classification accuracy increased at first and then decreased rapidly after reaching the summit when the superpixel size is 27. The classification accuracy of the combined use of optical and SAR data is higher than that using only Sentinel-2 MSI or Sentinel-1 C-SAR vertical transmitted and vertical received (VV) or vertical transmitted and horizontal received (VH) data, with overall accuracies of 0.8335, 0.8282, 0.7862, and 0.7886, respectively. Meanwhile, the results also indicate that classification based on superpixels obtained by SNIC significantly outperforms classification based on original pixels. The overall accuracy, producer accuracy, and user accuracy of SNIC superpixel-based classification increased by 9.14%, 17.16%, 27.35% and 1.36%, respectively, when compared with the pixel-based classification, based on the combination of optical and SAR data (using the random forest as the classifier). The results show that SNIC superpixel segmentation is a feasible method for high-resolution crop mapping based on multi-source remote sensing data. The automatic selection of the optimal superpixel size of SNIC will be focused on in future research.https://www.mdpi.com/2072-4292/13/6/1148high-resolutionSimple Non-Iterative Clusteringsuperpixel-based classificationsuperpixel sizemulti-source |
| spellingShingle | Lingbo Yang Limin Wang Ghali Abdullahi Abubakar Jingfeng Huang High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images Remote Sensing high-resolution Simple Non-Iterative Clustering superpixel-based classification superpixel size multi-source |
| title | High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images |
| title_full | High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images |
| title_fullStr | High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images |
| title_full_unstemmed | High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images |
| title_short | High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images |
| title_sort | high resolution rice mapping based on snic segmentation and multi source remote sensing images |
| topic | high-resolution Simple Non-Iterative Clustering superpixel-based classification superpixel size multi-source |
| url | https://www.mdpi.com/2072-4292/13/6/1148 |
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