Olivine exposures at the central peak of Copernicus crater of the Earth＇s Moon have been confirmed by telescope observations and Clementine spectra data. Using these exposures as training sites, this study used a met...Olivine exposures at the central peak of Copernicus crater of the Earth＇s Moon have been confirmed by telescope observations and Clementine spectra data. Using these exposures as training sites, this study used a method of combining two spectral indices （950 nm/750 nm and 2000 nm/1500 nm）, one maturity index （Is/FeO）, and four chemical content indices （FeO, Mg, Al, Ca）, through a decision tree classifier, to map olivine-rich units on the west lunar surface based on mosaicked Clementine image （500 m pixel size）. Most classified olivine exposures are found inside craters or on their rays, suggesting that olivine exposures are directly associated with the impact processes. The results have been validated in several selected sites, though further validations with data from the newest missions are strongly needed.展开更多
Data mining techniques are used to discover knowledge from GIS database in order to improve remote sensing image classification.Two learning granularities are proposed for inductive learning from spatial data,one is s...Data mining techniques are used to discover knowledge from GIS database in order to improve remote sensing image classification.Two learning granularities are proposed for inductive learning from spatial data,one is spatial object granularity,the other is pixel granularity.We also present an approach to combine inductive learning with conventional image classification methods,which selects class probability of Bayes classification as learning attributes.A land use classification experiment is performed in the Beijing area using SPOT multi_spectral image and GIS data.Rules about spatial distribution patterns and shape features are discovered by C5.0 inductive learning algorithm and then the image is reclassified by deductive reasoning.Comparing with the result produced only by Bayes classification,the overall accuracy increased by 11% and the accuracy of some classes,such as garden and forest,increased by about 30%.The results indicate that inductive learning can resolve spectral confusion to a great extent.Combining Bayes method with inductive learning not only improves classification accuracy greatly,but also extends the classification by subdividing some classes with the discovered knowledge.展开更多
This paper presents the comprehensive results of landing site topographic mapping and rover localization in Chang’e-3 mission.High-precision topographic products of the landing site with extremely high resolutions(up...This paper presents the comprehensive results of landing site topographic mapping and rover localization in Chang’e-3 mission.High-precision topographic products of the landing site with extremely high resolutions(up to 0.05 m)were generated from descent images and registered to CE-2 DOM.Local DEM and DOM with 0.02 m resolution were produced routinely at each waypoint along the rover traverse.The lander location was determined to be(19.51256°W,44.11884°N,-2615.451 m)using a method of DOM matching.In order to reduce error accumulation caused by wheel slippage and IMU drift in dead reckoning,cross-site visual localization and DOM matching localization methods were developed to localize the rover at waypoints;the overall traveled distance from the lander is 114.8 m from cross-site visual localization and 111.2 m from DOM matching localization.The latter is of highest accuracy and has been verified using a LRO NAC image where the rover trajeactory is directly identifiable.During CE-3 mission operations,landing site mapping and rover localization products including DEMs and DOMs,traverse maps,vertical traverse profiles were generated timely to support teleoperation tasks such as obstacle avoidance and rover path planning.展开更多
目前广泛应用的月球统一控制网2005(Unified Lunar Control Network 2005,ULCN2005)是由1994年的克莱门汀(Clementine)影像和之前的遥感数据联合平差构建的。提出利用21世纪获取的分辨率更高、精度更好的多探测任务数据,建立新一代月球...目前广泛应用的月球统一控制网2005(Unified Lunar Control Network 2005,ULCN2005)是由1994年的克莱门汀(Clementine)影像和之前的遥感数据联合平差构建的。提出利用21世纪获取的分辨率更高、精度更好的多探测任务数据,建立新一代月球控制网的方案与关键技术。该方案基于全球覆盖的月球遥感影像与激光高度计数据的联合平差,同时利用在月球轨道侦察器窄角相机影像上能高精度定位的绝对定位精度在厘米级的5个激光棱角反射标志点作为绝对控制。此外,还通过新的无线电测量方法对嫦娥三号着陆器进行高精度定位,将其定位结果也作为一个新的绝对控制数据。新一代控制网构建的重点有高精度的轨道器严密及通用成像几何模型的构建、多任务多模态数据间的多尺度特征提取与匹配、最优化多重覆盖影像的选择、全月球整体平差等。基于新的数据和技术,新一代月球控制网的精度和点的密度有望远超ULCN2005。展开更多
基金supported by the National High Technology Research and Development Program(No.2010AA12220101 and 2009AA12Z310)National Natural Science Foundation of China(No.40871202 and 41002120)
文摘Olivine exposures at the central peak of Copernicus crater of the Earth＇s Moon have been confirmed by telescope observations and Clementine spectra data. Using these exposures as training sites, this study used a method of combining two spectral indices （950 nm/750 nm and 2000 nm/1500 nm）, one maturity index （Is/FeO）, and four chemical content indices （FeO, Mg, Al, Ca）, through a decision tree classifier, to map olivine-rich units on the west lunar surface based on mosaicked Clementine image （500 m pixel size）. Most classified olivine exposures are found inside craters or on their rays, suggesting that olivine exposures are directly associated with the impact processes. The results have been validated in several selected sites, though further validations with data from the newest missions are strongly needed.
文摘Data mining techniques are used to discover knowledge from GIS database in order to improve remote sensing image classification.Two learning granularities are proposed for inductive learning from spatial data,one is spatial object granularity,the other is pixel granularity.We also present an approach to combine inductive learning with conventional image classification methods,which selects class probability of Bayes classification as learning attributes.A land use classification experiment is performed in the Beijing area using SPOT multi_spectral image and GIS data.Rules about spatial distribution patterns and shape features are discovered by C5.0 inductive learning algorithm and then the image is reclassified by deductive reasoning.Comparing with the result produced only by Bayes classification,the overall accuracy increased by 11% and the accuracy of some classes,such as garden and forest,increased by about 30%.The results indicate that inductive learning can resolve spectral confusion to a great extent.Combining Bayes method with inductive learning not only improves classification accuracy greatly,but also extends the classification by subdividing some classes with the discovered knowledge.
基金supported by the National Natural Science Foundation of China(Grant Nos.41201480,41171355 and 41301528)the Key Research Program of the Chinese Academy of Sciences(Grant No.KGZD-EW-603)
文摘This paper presents the comprehensive results of landing site topographic mapping and rover localization in Chang’e-3 mission.High-precision topographic products of the landing site with extremely high resolutions(up to 0.05 m)were generated from descent images and registered to CE-2 DOM.Local DEM and DOM with 0.02 m resolution were produced routinely at each waypoint along the rover traverse.The lander location was determined to be(19.51256°W,44.11884°N,-2615.451 m)using a method of DOM matching.In order to reduce error accumulation caused by wheel slippage and IMU drift in dead reckoning,cross-site visual localization and DOM matching localization methods were developed to localize the rover at waypoints;the overall traveled distance from the lander is 114.8 m from cross-site visual localization and 111.2 m from DOM matching localization.The latter is of highest accuracy and has been verified using a LRO NAC image where the rover trajeactory is directly identifiable.During CE-3 mission operations,landing site mapping and rover localization products including DEMs and DOMs,traverse maps,vertical traverse profiles were generated timely to support teleoperation tasks such as obstacle avoidance and rover path planning.
文摘目前广泛应用的月球统一控制网2005(Unified Lunar Control Network 2005,ULCN2005)是由1994年的克莱门汀(Clementine)影像和之前的遥感数据联合平差构建的。提出利用21世纪获取的分辨率更高、精度更好的多探测任务数据,建立新一代月球控制网的方案与关键技术。该方案基于全球覆盖的月球遥感影像与激光高度计数据的联合平差,同时利用在月球轨道侦察器窄角相机影像上能高精度定位的绝对定位精度在厘米级的5个激光棱角反射标志点作为绝对控制。此外,还通过新的无线电测量方法对嫦娥三号着陆器进行高精度定位,将其定位结果也作为一个新的绝对控制数据。新一代控制网构建的重点有高精度的轨道器严密及通用成像几何模型的构建、多任务多模态数据间的多尺度特征提取与匹配、最优化多重覆盖影像的选择、全月球整体平差等。基于新的数据和技术,新一代月球控制网的精度和点的密度有望远超ULCN2005。
