According to the remote sensing image characteristics, a set oi optimized compression quahty assessment methods is proposed on the basis of generating simulative images. Firstly, a means is put forward that generates ...According to the remote sensing image characteristics, a set oi optimized compression quahty assessment methods is proposed on the basis of generating simulative images. Firstly, a means is put forward that generates simulative images by scanning aerial films taking into account the space-borne remote sensing camera characteristics （including pixel resolution, histogram dynamic range and quantization）. In the course of compression quality assessment, the objective assessment considers images texture changes and mutual relationship between simulative images and decompressed ima- ges, while the synthesized estimation factor （SEF） is brought out innovatively for the first time. Subjective assessment adopts a display setup -- 0.5mrn/pixel, which considers human visual char- acteristic and mainstream monitor. The set of methods are applied in compression plan design of panchromatic camera loaded on ZY-1-02C satellite. Through systematic and comprehensive assess- ment, simulation results show that image compression quality with the compression ratio of d：l can meet the remote sensing application requirements.展开更多
The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry o...The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin.In this study,the arid remote sensing ecological index(ARSEI)for large-scale arid areas was developed,which coupled the information of the greenness index,the salinity index,the humidity index,the heat index,and the land degradation index of arid areas.The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019.The results show that the greenness index,the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin,while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment.The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement,followed by deterioration,and finally further improvement.The spatial variation of these changes was significant.From 2000 to 2019,grassland and wasteland(saline alkali land and sandy land)in the central and western parts of the basin had the worst ecological environment quality.The areas with poor ecological environment quality are mainly distributed in rivers,wetlands,and cultivated land around lakes.During the period from 2000 to 2019,except for the surrounding areas of the Aral Sea,the ecological environment quality in other areas of the Aral Sea Basin has been improved in general.The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were–0.593 and 0.524,respectively.Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin.However,human activities had a greater impact.The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas;it is very suitable for the study of the eco-environmental quality in arid areas.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
针对TDI推扫型光学遥感载荷在轨工作时可能出现的垂轨或沿轨方向图像质量下降现象,提出一种基于在轨MTF测试的单方向可定量图像质量提升方法。首先,采用刃边法测试得到沿轨和垂轨两个方向的MTF曲线,选取从零频到奈奎斯特频率范围内的n...针对TDI推扫型光学遥感载荷在轨工作时可能出现的垂轨或沿轨方向图像质量下降现象,提出一种基于在轨MTF测试的单方向可定量图像质量提升方法。首先,采用刃边法测试得到沿轨和垂轨两个方向的MTF曲线,选取从零频到奈奎斯特频率范围内的n个不同频率点,根据MTF目标值与实测值的比值确定各选定频率点的MTF提升倍数,依据频域响应特性并以提升倍数作为频域响应幅值构建空域卷积函数,同时结合在轨实测信噪比(Signal to Noise Ratio,SNR)构建抑噪函数,在确保SNR的前提下实现的MTF的定量提升。根据在轨试验验证结果表明,按照所提方法进行图像质量提升,在噪声抑制阈值0.5 dB范围内,垂轨方向MTF基本不变,沿轨方向奈奎斯特频率点的MTF提升2.51倍,获取的图像清晰度提升8.33%,证明该方法可有效实现定量图像质量提升。展开更多
基金Supported by the Civil Aerospace"The 12~(th) Five-year Plan"Advanced Research Project(No.D040103)
文摘According to the remote sensing image characteristics, a set oi optimized compression quahty assessment methods is proposed on the basis of generating simulative images. Firstly, a means is put forward that generates simulative images by scanning aerial films taking into account the space-borne remote sensing camera characteristics （including pixel resolution, histogram dynamic range and quantization）. In the course of compression quality assessment, the objective assessment considers images texture changes and mutual relationship between simulative images and decompressed ima- ges, while the synthesized estimation factor （SEF） is brought out innovatively for the first time. Subjective assessment adopts a display setup -- 0.5mrn/pixel, which considers human visual char- acteristic and mainstream monitor. The set of methods are applied in compression plan design of panchromatic camera loaded on ZY-1-02C satellite. Through systematic and comprehensive assess- ment, simulation results show that image compression quality with the compression ratio of d：l can meet the remote sensing application requirements.
基金This work was funded by the National Natural Science Foundation of China(U1603242)the Major Science and Technology Projects in Inner Mongolia,China(ZDZX2018054).
文摘The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin.In this study,the arid remote sensing ecological index(ARSEI)for large-scale arid areas was developed,which coupled the information of the greenness index,the salinity index,the humidity index,the heat index,and the land degradation index of arid areas.The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019.The results show that the greenness index,the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin,while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment.The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement,followed by deterioration,and finally further improvement.The spatial variation of these changes was significant.From 2000 to 2019,grassland and wasteland(saline alkali land and sandy land)in the central and western parts of the basin had the worst ecological environment quality.The areas with poor ecological environment quality are mainly distributed in rivers,wetlands,and cultivated land around lakes.During the period from 2000 to 2019,except for the surrounding areas of the Aral Sea,the ecological environment quality in other areas of the Aral Sea Basin has been improved in general.The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were–0.593 and 0.524,respectively.Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin.However,human activities had a greater impact.The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas;it is very suitable for the study of the eco-environmental quality in arid areas.
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。
文摘针对TDI推扫型光学遥感载荷在轨工作时可能出现的垂轨或沿轨方向图像质量下降现象,提出一种基于在轨MTF测试的单方向可定量图像质量提升方法。首先,采用刃边法测试得到沿轨和垂轨两个方向的MTF曲线,选取从零频到奈奎斯特频率范围内的n个不同频率点,根据MTF目标值与实测值的比值确定各选定频率点的MTF提升倍数,依据频域响应特性并以提升倍数作为频域响应幅值构建空域卷积函数,同时结合在轨实测信噪比(Signal to Noise Ratio,SNR)构建抑噪函数,在确保SNR的前提下实现的MTF的定量提升。根据在轨试验验证结果表明,按照所提方法进行图像质量提升,在噪声抑制阈值0.5 dB范围内,垂轨方向MTF基本不变,沿轨方向奈奎斯特频率点的MTF提升2.51倍,获取的图像清晰度提升8.33%,证明该方法可有效实现定量图像质量提升。
