自然资源遥感2026，Vol.38Issue(3)：75-81,7.DOI:10.6046/zrzyyg.2025194
基于SCNP-1DCNN-DRS网络模型的高光谱遥感岩性识别
Lithology identification through hyperspectral remote sensing based on the SCNP-1DCNN-DRS network model
摘要
Abstract
Hyperspectral remote sensing enables the simultaneous capture of spatial and spectral information.Based on this advantage and in combination with the significant learning capacity of deep learning technology,this study proposed a deep learning-based hyperspectral remote sensing approach for lithology identification,aiming to further enhance the quality and efficiency of lithologic mapping.Through a comprehensive analysis of the merits and demerits of existing lithology classification methods based on hyperspectral remote sensing and taking into account the geological attributes in lithologic mapping,this study proposed a higher-precision lithologic mapping model tailored to satellite hyperspectral remote sensing-SCNP-1DCNN-DRS.While inheriting the outstanding efficiency and feasibility possessed by the one-dimensional convolutional neural network(1DCNN)in lithologic mapping,the proposed SCNP-1DCNN-DRS network model introduced the learning strategy of spectral correlation of neighboring pixels(SCNP)to overcome the limitations of 1DCNN in lithologic mapping,such as the presence of numerous isolated noise pixels and imprecise boundary delineation.Additionally,a deep residual shrinkage(DRS)denoising module was integrated into the network to further mitigate the impacts of noise and redundant information in satellite hyperspectral images.Experimental validation demonstrates that the SCNP-1DCNN-DRS network model yielded an overall sample testing accuracy of 99.89%and an overall full-image testing accuracy of 83.41%.Compared to other models,the SCNP-1DCNN-DRS network model exhibits the highest accuracy in full-image testing,indicating its great application potential in lithologic mapping practices.关键词
SCNP/1DCNN/DRS/深度学习/高光谱遥感/资源一号02D/岩性填图Key words
spectral correlation of neighboring pixels(SCNP)/one-dimensional convolutional neural network(1DCNN)/deep residual shrinkage(DRS)/deep learning/hyperspectral remote sensing/ZY-1 02D/lithologic mapping分类
信息技术与安全科学引用本文复制引用
李娜,董新丰,李彤彤,武精凯,白伟,张睿,王文莉..基于SCNP-1DCNN-DRS网络模型的高光谱遥感岩性识别[J].自然资源遥感,2026,38(3):75-81,7.基金项目
中国地质调查局项目"基础地质遥感调查"(编号:DD20230011)、中国自然资源航空物探遥感中心青年创新基金课题"基于深度学习技术的高光谱遥感岩性填图方法研究"(编号:2023YFL27)和中国地质调查局项目"重点调查区航空高光谱遥感地质调查"(编号:DD20240120)共同资助. （编号:DD20230011）
