利用深度卷积神经网络分析植被对遥感图像中建筑物阴影提取的影响

IF 1 4区地球科学 Q4 GEOGRAPHY, PHYSICAL

Journal of Spatial Science Pub Date : 2024-09-03 DOI:10.1080/14498596.2024.2394439

Shuangquan Ge, Zihou Xu, Shaohan Cao, Dejun Feng, Wang Junfan

引用次数: 0

摘要

为了研究植被对阴影提取的影响，我们开发了一种抗干扰 DCNN，用于从 QuickBird 图像中提取阴影和植被。植被特征、颜色和几何形状对阴影提取有重要影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Analysis of vegetation influence on building shadow extraction in remote sensing imagery using deep convolutional neural networks

To study the influence of vegetation on shadow extraction, an anti-interference DCNN was developed to extract shadows and vegetation from QuickBird images. The vegetation features, color and geomet...

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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.