高分辨率遥感中的土地覆被分类：利用纹理特征进行 Swin Transformer 深度学习
IF 1
4区 地球科学
Q4 GEOGRAPHY, PHYSICAL
引用次数: 0
摘要
空间分辨率的提高改善了遥感图像传递的空间信息。然而，光谱-纹理组合特征对分类准确性的影响仍有待进一步研究。本文章由计算机程序翻译，如有差异，请以英文原文为准。
Land cover classification in high-resolution remote sensing: using Swin Transformer deep learning with texture features
The enhancement of spatial resolution has improved the spatial information conveyed by remote sensing images. Nevertheless, the impact of spectral-texture combined features on classification accura...
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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
25
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.
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