基于遥感影像和深度卷积神经网络的茶园遮荫树密度估算

IF 1 4区地球科学 Q4 GEOGRAPHY, PHYSICAL

Journal of Spatial Science Pub Date : 2021-12-27 DOI:10.1080/14498596.2021.2013966

A. Paul, Sayari Bhattacharyya, D. Chakraborty

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引用次数: 5

摘要

摘要一定数量的遮荫树密度对优质茶叶的生产至关重要。在此，基于深度卷积神经网络（DCNN）的架构用于检测和测量茶园高分辨率遥感（RS）图像中遮荫树的冠层面积，精度、召回率、F1得分和联合交集值分别为98.9%、85.1%、91.36和0.96。随后，以0.03的平均误差来估计荫蔽树密度。在本文中，建立了一个基于DCNN的全自动过程，该过程不仅可以检测RS图像中的遮荫树，还可以估计其冠层密度，以帮助茶园管理。

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Estimation of Shade Tree Density in Tea Garden using Remote Sensing Images and Deep Convolutional Neural Network

ABSTRACT A specific amount of shade tree density is essential for quality tea production. Here, deep convolutional neural network (DCNN) based architectures are used for detecting and measuring the canopy area of shade trees in high-resolution remote sensing (RS) images covering tea gardens with precision, recall, F1 score and Intersection-over-Union value of 98.9%, 85.1%, 91.36 and 0.96 respectively. Subsequently, shade tree density is estimated with average error of 0.03. In the present paper a fully automated DCNN-based process is established which not only detects shade trees in RS imagery, but also estimates their canopy density for assisting tea garden management.

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来源期刊

Journal of Spatial Science 地学-地质学

CiteScore

5.00

自引率

5.30%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of Spatial Science publishes papers broadly across the spatial sciences including such areas as cartography, geodesy, geographic information science, hydrography, digital image analysis and photogrammetry, remote sensing, surveying and related areas. Two types of papers are published by he journal: Research Papers and Professional Papers. Research Papers (including reviews) are peer-reviewed and must meet a minimum standard of making a contribution to the knowledge base of an area of the spatial sciences. This can be achieved through the empirical or theoretical contribution to knowledge that produces significant new outcomes. It is anticipated that Professional Papers will be written by industry practitioners. Professional Papers describe innovative aspects of professional practise and applications that advance the development of the spatial industry.