With the rapid development of satellite technology,the amount of remote sensing data and demand for remote sensing data analysis over large areas are greatly increasing.Hence,it is necessary to quickly filter out an o...With the rapid development of satellite technology,the amount of remote sensing data and demand for remote sensing data analysis over large areas are greatly increasing.Hence,it is necessary to quickly filter out an optimal dataset from massive dataset to support various remote sensing applications.However,with the improvements in temporal and spatial resolution,remote sensing data have become fragmented,which brings challenges to data retrieval.At present,most data service platforms rely on the query engines to retrieve data.Retrieval results still have a large amount of data with a high degree of overlap,which must be manually selected for further processing.This process is very labour-intensive and time-consuming.This paper proposes an improved coverage-oriented retrieval algorithm that aims to retrieve an optimal image combination with the minimum number of images closest to the imaging time of interest while maximized covering the target area.The retrieval efficiency of this algorithm was analysed by applying different implementation practices:Arcpy,PyQGIS,and GeoPandas.The experimental results confirm the effectiveness of the algorithm and suggest that the GeoPandas-based approach is most advantageous when processing large-area data.展开更多
高光谱遥感数据能够提供比多光谱遥感数据更为丰富的光谱信息,从而更精确地刻画地物的光谱特征。在水体遥感原理基础上,采用自适应波段选择(adaptive band selection,ABS)方法对HJ-1A卫星高光谱数据的波段相关性和信息量进行分析,结合B...高光谱遥感数据能够提供比多光谱遥感数据更为丰富的光谱信息,从而更精确地刻画地物的光谱特征。在水体遥感原理基础上,采用自适应波段选择(adaptive band selection,ABS)方法对HJ-1A卫星高光谱数据的波段相关性和信息量进行分析,结合BP神经网络技术确定最优波段组合并构建盐湖矿物离子含量的反演模型,对柴达木盆地西台吉乃尔湖的K+,Mg2+,Na+,Cl-和SO2-4离子含量进行定量反演,获得盐湖矿物离子含量的空间分布情况。研究结果表明,BP神经网络反演模型的盐湖矿物离子含量反演精度在85%以上,反演得到的矿物离子含量的分布情况与实地调查结果基本一致。因此,利用高光谱数据和BP神经网络可以对盐湖矿物资源进行大范围动态监测,为盐湖资源的合理开发和高效利用提供科学依据。展开更多
基金supported by National Key R&D Program for Intergovernmental International Innovation Cooperation(number 2018YFE0100100).
文摘With the rapid development of satellite technology,the amount of remote sensing data and demand for remote sensing data analysis over large areas are greatly increasing.Hence,it is necessary to quickly filter out an optimal dataset from massive dataset to support various remote sensing applications.However,with the improvements in temporal and spatial resolution,remote sensing data have become fragmented,which brings challenges to data retrieval.At present,most data service platforms rely on the query engines to retrieve data.Retrieval results still have a large amount of data with a high degree of overlap,which must be manually selected for further processing.This process is very labour-intensive and time-consuming.This paper proposes an improved coverage-oriented retrieval algorithm that aims to retrieve an optimal image combination with the minimum number of images closest to the imaging time of interest while maximized covering the target area.The retrieval efficiency of this algorithm was analysed by applying different implementation practices:Arcpy,PyQGIS,and GeoPandas.The experimental results confirm the effectiveness of the algorithm and suggest that the GeoPandas-based approach is most advantageous when processing large-area data.
文摘高光谱遥感数据能够提供比多光谱遥感数据更为丰富的光谱信息,从而更精确地刻画地物的光谱特征。在水体遥感原理基础上,采用自适应波段选择(adaptive band selection,ABS)方法对HJ-1A卫星高光谱数据的波段相关性和信息量进行分析,结合BP神经网络技术确定最优波段组合并构建盐湖矿物离子含量的反演模型,对柴达木盆地西台吉乃尔湖的K+,Mg2+,Na+,Cl-和SO2-4离子含量进行定量反演,获得盐湖矿物离子含量的空间分布情况。研究结果表明,BP神经网络反演模型的盐湖矿物离子含量反演精度在85%以上,反演得到的矿物离子含量的分布情况与实地调查结果基本一致。因此,利用高光谱数据和BP神经网络可以对盐湖矿物资源进行大范围动态监测,为盐湖资源的合理开发和高效利用提供科学依据。
