A feature filtering-based transfer network for few-shot soil organic carbon content estimation with spectral data

IF 1.4 4区地球科学 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

Remote Sensing Letters Pub Date : 2023-11-27 DOI:10.1080/2150704x.2023.2282401

Wudi Zhao, Zhilu Wu, Zhendong Yin, Dasen Li

引用次数: 0

Abstract

At present, the deep learning method relies on its strong feature extraction ability and has been successfully applied to the estimation of soil organic carbon (SOC) content with large-scale hypers...

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一种基于特征滤波的基于光谱数据的土壤有机碳含量估算转移网络

目前，深度学习方法依靠其强大的特征提取能力，已成功应用于大规模超尺度土壤有机碳(SOC)含量估算。

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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来源期刊

Remote Sensing Letters REMOTE SENSING-IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

CiteScore

4.10

自引率

4.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Remote Sensing Letters is a peer-reviewed international journal committed to the rapid publication of articles advancing the science and technology of remote sensing as well as its applications. The journal originates from a successful section, of the same name, contained in the International Journal of Remote Sensing from 1983 –2009. Articles may address any aspect of remote sensing of relevance to the journal’s readership, including – but not limited to – developments in sensor technology, advances in image processing and Earth-orientated applications, whether terrestrial, oceanic or atmospheric. Articles should make a positive impact on the subject by either contributing new and original information or through provision of theoretical, methodological or commentary material that acts to strengthen the subject.