干旱区生态环境知识资源中心(Arid): Early Identification of Cotton Fields Based on Gf-6 Images in Arid and Semiarid Regions (China)

Arid

DOI	10.3390/rs15225326
	Early Identification of Cotton Fields Based on Gf-6 Images in Arid and Semiarid Regions (China)
	Zou, Chen; Chen, Donghua; Chang, Zhu; Fan, Jingwei; Zheng, Jian; Zhao, Haiping; Wang, Zuo; Li, Hu
通讯作者	Li, H
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:22
英文摘要	Accurately grasping the distribution and area of cotton for agricultural irrigation scheduling, intensive and efficient management of water resources, and yield estimation in arid and semiarid regions is of great significance. In this paper, taking the Xinjiang Shihezi oasis agriculture region as the study area, extracting the spectroscopic characterization (R, G, B, panchromatic), texture feature (entropy, mean, variance, contrast, homogeneity, angular second moment, correlation, and dissimilarity) and characteristics of vegetation index (normalized difference vegetation index/NDVI, ratio vegetation index/DVI, difference vegetation index/RVI) in the cotton flowering period before and after based on GF-6 image data, four models such as the random forests (RF) and deep learning approach (U-Net, DeepLabV3+ network, Deeplabv3+ model based on attention mechanism) were used to identify cotton and to compare their accuracies. The results show that the deep learning model is better than that of the random forest model. In all the deep learning models with three kinds of feature sets, the recognition accuracy and credibility of the DeepLabV3+ model based on the attention mechanism are the highest, the overall recognition accuracy of cotton is 98.23%, and the kappa coefficient is 96.11. Using the same Deeplabv3+ model based on an attention mechanism with different input feature sets (all features and only spectroscopic characterization), the identification accuracy of the former is much higher than that of the latter. GF-6 satellite image data in the field of crop type recognition has great application potential and prospects.
英文关键词	remote sensing identification GF-6 satellite cotton deep learning
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001123365800001
WOS关键词	CROP CLASSIFICATION ; AGRICULTURAL CROPS ; ALGORITHMS ; MODEL
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398346
推荐引用方式 GB/T 7714	Zou, Chen,Chen, Donghua,Chang, Zhu,et al. Early Identification of Cotton Fields Based on Gf-6 Images in Arid and Semiarid Regions (China)[J],2023,15(22).
APA	Zou, Chen.,Chen, Donghua.,Chang, Zhu.,Fan, Jingwei.,Zheng, Jian.,...&Li, Hu.(2023).Early Identification of Cotton Fields Based on Gf-6 Images in Arid and Semiarid Regions (China).REMOTE SENSING,15(22).
MLA	Zou, Chen,et al."Early Identification of Cotton Fields Based on Gf-6 Images in Arid and Semiarid Regions (China)".REMOTE SENSING 15.22(2023).