干旱区生态环境知识资源中心(Arid): Object-oriented multi-scale segmentation and multi-feature fusion-based method for identifying typical fruit trees in arid regions using Sentinel-1/2 satellite images

Arid

DOI	10.1038/s41598-024-68991-7
	Object-oriented multi-scale segmentation and multi-feature fusion-based method for identifying typical fruit trees in arid regions using Sentinel-1/2 satellite images
	Liang, Jiaxi; Sawut, Mamat; Cui, Jintao; Hu, Xin; Xue, Zijing; Zhao, Ming; Zhang, Xinyu; Rouzi, Areziguli; Ye, Xiaowen; Xilike, Aerqing
通讯作者	Sawut, M
来源期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
出版年	2024
卷号	14 期号:1
英文摘要	Fruit tree identification that is quick and precise lays the groundwork for scientifically evaluating orchard yields and dynamically monitoring planting areas. This study aims to evaluate the applicability of time series Sentinel-1/2 satellite data for fruit tree classification and to provide a new method for accurately extracting fruit tree species. Therefore, the study area selected is the Tarim Basin, the most important fruit-growing region in northwest China. The main focus is on identifying several major fruit tree species in this region. Time series Sentinel-1/2 satellite images acquired from the Google Earth Engine (GEE) platform are used for the study. A multi-scale segmentation approach is applied, and six categories of features including spectral, phenological, texture, polarization, vegetation index, and red edge index features are constructed. A total of forth-four features are extracted and optimized using the Vi feature importance index to determine the best time phase. Based on this, an object-oriented (OO) segmentation combined with the Random Forest (RF) method is used to identify fruit tree species. To find the best method for fruit tree identification, the results are compared with three other widely used traditional machine learning algorithms: Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), and Classification and Regression Tree (CART). The results show that: (1) the object-oriented segmentation method helps to improve the accuracy of fruit tree identification features, and September satellite images provide the best time window for fruit tree identification, with spectral, phenological, and texture features contributing the most to fruit tree species identification. (2) The RF model has higher accuracy in identifying fruit tree species than other machine learning models, with an overall accuracy (OA) and a kappa coefficient (KC) of 94.60% and 93.74% respectively, indicating that the combination of object-oriented segmentation and RF algorithm has great value and potential for fruit tree identification and classification. This method can be applied to large-scale fruit tree remote sensing classification and provides an effective technical means for monitoring fruit tree planting areas using medium-to-high-resolution remote sensing images.
英文关键词	GEE Object-oriented Feature optimization Machine learning Fruit tree recognition
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:001285457700094
WOS关键词	BIG DATA APPLICATIONS ; GOOGLE EARTH ENGINE ; TIME-SERIES ; RED-EDGE ; CLASSIFICATION ; REFLECTANCE ; EXTRACTION ; SYSTEM ; LEAVES
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405581
推荐引用方式 GB/T 7714	Liang, Jiaxi,Sawut, Mamat,Cui, Jintao,et al. Object-oriented multi-scale segmentation and multi-feature fusion-based method for identifying typical fruit trees in arid regions using Sentinel-1/2 satellite images[J],2024,14(1).
APA	Liang, Jiaxi.,Sawut, Mamat.,Cui, Jintao.,Hu, Xin.,Xue, Zijing.,...&Xilike, Aerqing.(2024).Object-oriented multi-scale segmentation and multi-feature fusion-based method for identifying typical fruit trees in arid regions using Sentinel-1/2 satellite images.SCIENTIFIC REPORTS,14(1).
MLA	Liang, Jiaxi,et al."Object-oriented multi-scale segmentation and multi-feature fusion-based method for identifying typical fruit trees in arid regions using Sentinel-1/2 satellite images".SCIENTIFIC REPORTS 14.1(2024).