Limited-Samples-Based Crop Classification Using a Time-Weighted Dynamic Time Warping Method, Sentinel-1 Imagery, and Google Earth Engine
文献类型：期刊论文
| 作者 | Xiao, Xingyuan; Jiang, Linlong; Liu, Yaqun3; Ren, Guozhen2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-02-01 |
| 卷号 | 15期号:4 |
| 关键词 | Sentinel-1 SAR time series sampling strategy time-weighted dynamic time warping simple non-iterative clustering Google Earth Engine object-based crop classification |
| ISSN号 | 2072-4292 |
| DOI | 10.3390/rs15041112 |
| 文献子类 | Article |
| 英文摘要 | Reliable crop type classification supports the scientific basis for food security and sustainable agricultural development. However, it still lacks a limited-samples-based crop classification method which is labor- and time-efficient. To this end, we used the Google Earth Engine (GEE) and Sentinel-1A/B SAR time series to develop eight types of crop classification strategies based on different sampling methods of central and scattered, different perspectives of object-based and pixel-based, and different classifiers of the Time-Weighted Dynamic Time Warping (TWDTW) and Random Forest (RF). We carried out 30-times classifications with different samples for each strategy to classify the crop types at the North Dakota-Minnesota border in the U.S. We then compared their classification accuracies and assessed the accuracy sensitivity to sample size. The results found that the TWDTW generally performed better than RF, especially for small-sample classification. Object-based classifications had higher accuracies than pixel-based classifications, and the object-based TWDTW had the highest accuracy. RF performed better in scattered sampling than the central sampling strategy. TWDTW performed better than RF in distinguishing soybean and dry bean with similar curves. The accuracies improved for all eight classification strategies with increasing sample size, and TWDTW was more robust, while RF was more sensitive to sample size change. RF required many more samples than TWDTW to achieve satisfactory accuracy, and it performed better than TWDTW when the sample size exceeded 50. The accuracy comparisons indicated that the TWDTW has stronger temporal and spatial generalization capabilities and has high potential applications for early, historical, and limited-samples-based crop type classification. The findings of our research are worthwhile contributions to the methodology and practices of crop type classification as well as sustainable agricultural development. |
| WOS关键词 | LAND-COVER CLASSIFICATION ; RANDOM FOREST ; SERIES DATA ; VEGETATION ; PROGRAM |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:000942301800001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/190318]  |
| 专题 | 区域可持续发展分析与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Shandong Ruizhi Flight Control Technol Co Ltd, Qingdao 266500, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Reg Sustainable Dev Modeling, Beijing 100101, Peoples R China 3.Shandong Univ Sci & Technol, Coll Geodesy & Geomatics, Qingdao 266590, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xiao, Xingyuan,Jiang, Linlong,Liu, Yaqun,et al. Limited-Samples-Based Crop Classification Using a Time-Weighted Dynamic Time Warping Method, Sentinel-1 Imagery, and Google Earth Engine[J]. REMOTE SENSING,2023,15(4). |
| APA | Xiao, Xingyuan,Jiang, Linlong,Liu, Yaqun,&Ren, Guozhen.(2023).Limited-Samples-Based Crop Classification Using a Time-Weighted Dynamic Time Warping Method, Sentinel-1 Imagery, and Google Earth Engine.REMOTE SENSING,15(4). |
| MLA | Xiao, Xingyuan,et al."Limited-Samples-Based Crop Classification Using a Time-Weighted Dynamic Time Warping Method, Sentinel-1 Imagery, and Google Earth Engine".REMOTE SENSING 15.4(2023). |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。