高空间分辨率卫星影像的融合研究一直备受关注。本文以WorldView-2全色、多光谱影像为数据源,采用主成分分析、小波-主成分分析、高通滤波、HCS(Hypersherical color space)四种融合方法进行实验,并对融合效果做出定性及定量评价。研究...高空间分辨率卫星影像的融合研究一直备受关注。本文以WorldView-2全色、多光谱影像为数据源,采用主成分分析、小波-主成分分析、高通滤波、HCS(Hypersherical color space)四种融合方法进行实验,并对融合效果做出定性及定量评价。研究结果表明,HCS法不仅显著提高影像空间细节表现力,而且有效地保持多光谱影像的光谱信息,其融合影像质量最高。展开更多
Accurate landslide extraction is significant for landslide disaster prevention and control.Remote sensing images have been widely used in landslide investigation,and landslide extraction methods based on deep learning...Accurate landslide extraction is significant for landslide disaster prevention and control.Remote sensing images have been widely used in landslide investigation,and landslide extraction methods based on deep learning combined with remote sensing images(such as U-Net)have received a lot of attention.However,because of the variable shape and texture features of landslides in remote sensing images,the rich spectral features,and the complexity of their surrounding features,landslide extraction using U-Net can lead to problems such as false detection and missed detection.Therefore,this study introduces the channel attention mechanism called the squeeze-and-excitation network(SENet)in the feature fusion part of U-Net;the study also constructs an attention U-Net landside extraction model combining SENet and U-Net,and uses Sentinel-2A remote sensing images for model training and validation.The extraction results are evaluated through different evaluation metrics and compared with those of two models:U-Net and U-Net Backbone(U-Net Without Skip Connection).The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%,which is about 2%and 3%higher than U-Net and U-Net Backbone,respectively,with less false detection and more accurate extraction results.展开更多
本研究基于水稻孕穗期、抽穗期、灌浆期和成熟期4个生育期的Sentinel-2遥感数据,分析各生育期内卫星遥感光谱参数与稻米品质指标的关系,建立基于各生育期卫星光谱信息的水稻品质指标预测模型。将5种稻米品质指标分别与4个生育期内的光...本研究基于水稻孕穗期、抽穗期、灌浆期和成熟期4个生育期的Sentinel-2遥感数据,分析各生育期内卫星遥感光谱参数与稻米品质指标的关系,建立基于各生育期卫星光谱信息的水稻品质指标预测模型。将5种稻米品质指标分别与4个生育期内的光谱参数进行皮尔逊相关性分析,结果表明,5项品质指标在4个生育期内均与光谱参数有不同程度相关性。然后筛选出相关性效果显著的光谱参数,用于建立各品质指标的预测方程,建模结果表明,基于卫星遥感光谱信息解释率由大到小的稻米品质指标依次是精米率>长宽比>蛋白质含量>直链淀粉含量>糙米率;卫星遥感光谱反演稻米各品质指标所在的最佳生育期不同,糙米率和精米率的最佳生育期为抽穗期,其建模决定系数(Coefficient of Determination,R^(2))分别为0.461和0.893;长宽比的最佳生育期为成熟期,R^(2)为0.878;直链淀粉含量和蛋白质含量的最佳生育期为灌浆期,R^(2)分别为0.646和0.647;基于卫星遥感光谱信息的稻米品质模型验证效果较好,解释率为51%~74%。可见,利用卫星遥感技术能够实现大范围水稻品质指标定量监测与评估。展开更多
文摘高空间分辨率卫星影像的融合研究一直备受关注。本文以WorldView-2全色、多光谱影像为数据源,采用主成分分析、小波-主成分分析、高通滤波、HCS(Hypersherical color space)四种融合方法进行实验,并对融合效果做出定性及定量评价。研究结果表明,HCS法不仅显著提高影像空间细节表现力,而且有效地保持多光谱影像的光谱信息,其融合影像质量最高。
基金supported by the Project Supported by the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources[grant number KF-2021-06-014]the National Natural Scientific Foundation of China[grant number 42201459]+2 种基金the Central Government to Guide Local Scientific and Technological Development[grant number 22ZY1QA005]Tianyou Youth Talent Lift Program of Lanzhou Jiaotong University,Young Doctoral Fund Project of Higher Education Institutions in Gansu Province[grant number 2022QB-058]State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR,CASM(2022-03-03).
文摘Accurate landslide extraction is significant for landslide disaster prevention and control.Remote sensing images have been widely used in landslide investigation,and landslide extraction methods based on deep learning combined with remote sensing images(such as U-Net)have received a lot of attention.However,because of the variable shape and texture features of landslides in remote sensing images,the rich spectral features,and the complexity of their surrounding features,landslide extraction using U-Net can lead to problems such as false detection and missed detection.Therefore,this study introduces the channel attention mechanism called the squeeze-and-excitation network(SENet)in the feature fusion part of U-Net;the study also constructs an attention U-Net landside extraction model combining SENet and U-Net,and uses Sentinel-2A remote sensing images for model training and validation.The extraction results are evaluated through different evaluation metrics and compared with those of two models:U-Net and U-Net Backbone(U-Net Without Skip Connection).The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%,which is about 2%and 3%higher than U-Net and U-Net Backbone,respectively,with less false detection and more accurate extraction results.
文摘本研究基于水稻孕穗期、抽穗期、灌浆期和成熟期4个生育期的Sentinel-2遥感数据,分析各生育期内卫星遥感光谱参数与稻米品质指标的关系,建立基于各生育期卫星光谱信息的水稻品质指标预测模型。将5种稻米品质指标分别与4个生育期内的光谱参数进行皮尔逊相关性分析,结果表明,5项品质指标在4个生育期内均与光谱参数有不同程度相关性。然后筛选出相关性效果显著的光谱参数,用于建立各品质指标的预测方程,建模结果表明,基于卫星遥感光谱信息解释率由大到小的稻米品质指标依次是精米率>长宽比>蛋白质含量>直链淀粉含量>糙米率;卫星遥感光谱反演稻米各品质指标所在的最佳生育期不同,糙米率和精米率的最佳生育期为抽穗期,其建模决定系数(Coefficient of Determination,R^(2))分别为0.461和0.893;长宽比的最佳生育期为成熟期,R^(2)为0.878;直链淀粉含量和蛋白质含量的最佳生育期为灌浆期,R^(2)分别为0.646和0.647;基于卫星遥感光谱信息的稻米品质模型验证效果较好,解释率为51%~74%。可见,利用卫星遥感技术能够实现大范围水稻品质指标定量监测与评估。
