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Mapping Fruit-Tree Plantation Using Sentinel-1/2 Time Series Images with Multi-Index Entropy Weighting Dynamic Time Warping Method

文献类型： 外文期刊

作者： Xu, Weimeng 1 ; Li, Zhenhong 2 ; Lin, Hate 2 ; Shao, Guowen 1 ; Zhao, Fa 1 ; Wang, Han 1 ; Cheng, Jinpeng 6 ; Lei, Lei 2 ; Chen, Riqiang 1 ; Han, Shaoyu 1 ; Yang, Hao 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

2.Changan Univ, Coll Geol Engn & Geomatics, Xian 710054, Peoples R China

3.Key Lab Loess, Xian 710054, Peoples R China

4.Changan Univ, Big Data Ctr Geosci & Satellites BDCGS, Xian 710054, Peoples R China

5.Minist Educ, Key Lab Western Chinas Mineral Resources & Geol En, Xian 710054, Peoples R China

6.Henan Agr Univ, Coll Agron, Zhengzhou 450046, Peoples R China

关键词： orchard mapping; DTW; satellite image series; Sentinel-1; Sentinel-2

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2024 年 16 卷 18 期

页码：

收录情况： SCI

摘要： Plantation distribution information is of great significance to the government's macro-control, optimization of planting layout, and realization of efficient agricultural production. Existing studies primarily relied on high spatiotemporal resolution remote sensing data to address same-spectrum, different-object classification by extracting phenological information from temporal imagery. However, the classification problem of orchard or artificial forest, where the spectral and textural features are similar and their phenological characteristics are alike, still presents a substantial challenge. To address this challenge, we innovatively proposed a multi-index entropy weighting DTW method (ETW-DTW), building upon the traditional DTW method with single-feature inputs. In contrast to previous DTW classification approaches, this method introduces multi-band information and utilizes entropy weighting to increase the inter-class distances. This allowed for accurate classification of orchard categories, even in scenarios where the spectral textures were similar and the phenology was alike. We also investigated the impact of fusing optical and Synthetic Aperture Radar (SAR) data on the classification accuracy. By combining Sentinel-1 and Sentinel-2 time series imagery, we validated the enhanced classification effectiveness with the inclusion of SAR data. The experimental results demonstrated a noticeable improvement in orchard classification accuracy under conditions of similar spectral characteristics and phenological patterns, providing comprehensive information for orchard mapping. Additionally, we further explored the improvement in results based on two different parcel-based classification strategies compared to pixel-based classification methods. By comparing the classification results, we found that the parcel-based averaging method has advantages in clearly defining orchard boundaries and reducing noise interference. In conclusion, the introduction of the ETW-DTW method is of significant practical importance in addressing the challenge of same-spectrum, different-object classification. The obtained orchard distribution can provide valuable information for the government to optimize the planting structure and layout and regulate the macroeconomic benefits of the fruit industry.

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