A Fusion Method for Multisource Land Cover Products Based on Superpixels and Statistical Extraction for Enhancing Resolution and Improving Accuracy
文献类型：期刊论文
| 作者 | Jin, Qi1; Xu, Erqi2; Zhang, Xuqing1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-04-01 |
| 卷号 | 14期号:7页码:21 |
| 关键词 | land cover multisource information fusion Google Earth Engine superpixels large-area remote sensing products |
| DOI | 10.3390/rs14071676 |
| 通讯作者 | Xu, Erqi(xueq@igsnrr.ac.cn) |
| 英文摘要 | The discrepancies in existing land cover data are relatively high, indicating low local precision and application limitations. Multisource data fusion is an effective way to solve this problem; however, the fusion procedure often requires resampling to unify the spatial resolution, causing a lower spatial resolution. To solve this problem, this study proposes a multisource product fusion mapping method of filtering training samples and product correction at a fine resolution. Based on the Superpixel algorithm, principal component analysis (PCA), and statistical extraction techniques, combined with the Google Earth Engine (GEE) platform, reliable land cover data were acquired. GEE and machine-learning algorithms correct the unreliable information of multiple products into a new land cover fusion result. Compared to the common method of extracting consistent pixels from existing products, our proposed method effectively removes nearly 38.75% of them, with a high probability of classification error. The overall accuracy of fusion in this study reached 85.80%, and the kappa coefficient reached 0.82, with an overall accuracy improvement of 11.75-24.17% and a kappa coefficient improvement of 0.16 to 0.3 compared to other products. For existing single-category products, we corrected the phenomenon of overinterpretation in inconsistent areas; the overall accuracy improvement ranged from 2.99% to 20.71%, while the kappa coefficient improvement ranged from 0.22 to 0.56. Thus, our proposed method can combine information from multiple products and serve as an effective method for large areas and even as a global land cover fusion product. |
| WOS关键词 | RANDOM FOREST CLASSIFIER ; TIME-SERIES ; VEGETATION ; INDEX ; WATER ; TM ; SEGMENTATION ; BIODIVERSITY ; SOUTHEAST ; EXPANSION |
| 资助项目 | Second Tibetan Plateau Scientific Expedition and Research Program (STEP)[2019QZKK0603] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA20040201] ; Youth Innovation Promotion Association CAS[2021052] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000780902800001 |
| 出版者 | MDPI |
| 资助机构 | Second Tibetan Plateau Scientific Expedition and Research Program (STEP) ; Strategic Priority Research Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/173969]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Xu, Erqi |
| 作者单位 | 1.Jilin Univ, Coll Geoexplorat Sci & Technol, Changchun 130026, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jin, Qi,Xu, Erqi,Zhang, Xuqing. A Fusion Method for Multisource Land Cover Products Based on Superpixels and Statistical Extraction for Enhancing Resolution and Improving Accuracy[J]. REMOTE SENSING,2022,14(7):21. |
| APA | Jin, Qi,Xu, Erqi,&Zhang, Xuqing.(2022).A Fusion Method for Multisource Land Cover Products Based on Superpixels and Statistical Extraction for Enhancing Resolution and Improving Accuracy.REMOTE SENSING,14(7),21. |
| MLA | Jin, Qi,et al."A Fusion Method for Multisource Land Cover Products Based on Superpixels and Statistical Extraction for Enhancing Resolution and Improving Accuracy".REMOTE SENSING 14.7(2022):21. |
入库方式： OAI收割
来源：地理科学与资源研究所
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