Land cover classification in high-resolution remote sensing: using Swin Transformer deep learning with texture features

IF 1 4区 地球科学 Q4 GEOGRAPHY, PHYSICAL Journal of Spatial Science Pub Date : 2024-08-07 DOI:10.1080/14498596.2024.2386317
Yongle Zhang, Min Huang, Yanxi Chen, Xingzhu Xiao, Hao Li
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Abstract

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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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
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。
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来源期刊
Journal of Spatial Science
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.
期刊最新文献
Analysis of vegetation influence on building shadow extraction in remote sensing imagery using deep convolutional neural networks A novel approach to coral species classification using deep learning and unsupervised feature extraction Land cover classification in high-resolution remote sensing: using Swin Transformer deep learning with texture features A change detection algorithm for the SAR images based on DWT and DE optimization Predicting land use and land cover change dynamics in the eThekwini Municipality: a machine learning approach with Landsat imagery
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