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基于\"嫦娥一号\"激光测高数据的月球极区光照条件研究 总被引：1，自引：0，他引：1
月球极区光照模型为研究月球车着陆点选择和水冰存在的探测提供了依据.利用\"嫦娥一号\"探月卫星获取的激光测高数据,得到了全月面高精度的数字高程模型(DEM),特别是在月球两极地区,地形细节清晰可见,为极区光照模型的建立提供了精确的数据基础.本文通过由测高数据建立的DEM和月球轨道参数相结合,采用地形最大高度角法,对月球极区的光照条件及其应用进行了研究和分析.光照率的计算周期为19年,考虑了黄道和白道交点进动的影响(18.6年).计算结果表明:(1)未发现有持续光照区;(2)有长久阴影区存在;(3)在南极或北极的夏季,撞击坑边沿高地处可以享受到连续的光照;(4)Shackleton撞击坑可以作为月球车着陆的首选目标之一. 相似文献
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Unequal illumination of the subsurface highly impacts the quality of seismic imaging. Different image points receive different folds of reflection‐angle illumination, which can be caused by irregular acquisition or by wave propagation in complex media. Illumination problems can deteriorate amplitudes in migrated images. To address this problem, we present a method of stacking angle‐domain common‐image gathers, in which we use local similarity with soft thresholding to determine the folds of local illumination. Normalization by local similarity regularizes local illumination of reflection angles for each image point of the subsurface model. This approach compensates for irregular illumination by selective stacking in the image space, regardless of the cause of acquisition or propagation irregularities. Additional migration is not required because the methodology is implemented in the reflection angle domain after migration. We use two synthetic examples to demonstrate that our method can normalize migration amplitudes and effectively suppress migration artefacts. 相似文献
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In this study, we compare three commonly used methods for hyperspectral image classification, namely Support Vector Machines (SVMs), Gaussian Processes (GPs) and the Spectral Angle Mapper (SAM). We assess their performance in combination with different kernels (i.e. which use distance-based and angle-based metrics). The assessment is done in two experiments, under ideal conditions in the laboratory and, separately, in the field (an operational open pit mine) using natural light. For both experiments independent training and test sets are used. Results show that GPs generally outperform the SVMs, irrespective of the kernel used. Furthermore, angle-based methods, including the Spectral Angle Mapper, outperform GPs and SVMs when using distance-based (i.e. stationary) kernels in the field experiment. A new GP method using an angle-based (i.e. a non-stationary) kernel – the Observation Angle Dependent (OAD) covariance function – outperforms SAM and SVMs in both experiments using only a small number of training spectra. These findings show that distance-based kernels are more affected by changes in illumination between the training and test set than are angular-based methods/kernels. Taken together, this study shows that independent training data can be used for classification of hyperspectral data in the field such as in open pit mines, by using Bayesian machine-learning methods and non-stationary kernels such as GPs and the OAD kernel. This provides a necessary component for automated classifications, such as autonomous mining where many images have to be classified without user interaction. 相似文献
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