Single crater-aided inertial navigation for autonomous asteroid landing

In this paper, a novel crater-aided inertial navigation approach for autonomous asteroid landing mission is developed. It overcomes the major deficiencies of existing approaches in the literature, which mainly focuses on the case where craters are abundant in the camera field of view. As a result, traditional crater based methods require at least three craters to achieve crater matching, which limits their application in final landing phase where craters are scarce in the camera’s field of view. In contrast, the proposed algorithm enables single crater based crater matching based on a novel 2D-3D crater re-projection model. The re-projection model adopts inertial measurements as a reference, and re-projects the 3D crater model onto descent images to achieve the matching to its counterpart. An asteroid landing simulation toolbox is developed to validate the performance of the proposed approach. Through comparison with the state-of-the-art local image feature and crater based navigation algorithms, the proposed approach is validated to achieve a competitive performance in terms of feature matching and pose estimation accuracy with a much lighter computational cost.     

A machine learning approach to crater detection from topographic data

Craters are distinctive features on the surfaces of most terrestrial planets. Craters reveal the relative ages of surface units and provide information on surface geology. Extracting craters is one of the fundamental tasks in planetary research. Although many automated crater detection algorithms have been developed to exact craters from image or topographic data, most of them are applicable only in particular regions, and only a few can be widely used, especially in complex surface settings. In this study, we present a machine learning approach to crater detection from topographic data. This approach includes two steps: detecting square regions which contain one crater with the use of a boosting algorithm and delineating the rims of the crater in each square region by local terrain analysis and circular Hough transform. A new variant of Haar-like features (scaled Haar-like features) is proposed and combined with traditional Haar-like features and local binary pattern features to enhance the performance of the classifier. Experimental results with the use of Mars topographic data demonstrate that the developed approach can significantly decrease the false positive detection rate while maintaining a relatively high true positive detection rate even in challenging sites.     

Bayesian network-based extraction of lunar impact craters from optical images and DEM data

Impact craters are among the most noticeable geomorphological features on the planetary surface and yield significant information about terrain evolution and the history of the solar system. Thus, the recognition of impact craters is an important branch of modern planetary studies. Aiming at addressing problems associated with the insufficient and inaccurate detection of lunar impact craters, a decision fusion method within the Bayesian network (BN) framework is developed in this paper to handle multi-source information from both optical images and associated digital elevation model (DEM) data. First, we implement the edge-based method for efficiently searching crater candidates which are the image patches that can potentially contain impact craters. Secondly, the multi-source representations of an impact crater derived from both optical images and DEM data are proposed and constructed to quantitatively describe the two-dimensional (2D) and three-dimensional (3D) morphology, consisting of Histogram of Oriented Gradient (HOG), Histogram of Multi-scale Slope (HMS) and Histogram of Multi-scale Aspect (HMA). Finally, a BN-based framework integrates the multi-source representations of impact craters, which can provide reductant and complementary information, for distinguishing craters from non-craters. To evaluate the effectiveness and robustness of the proposed method, experiments were conducted on three lunar scenes using both orthoimages from the Lunar Reconnaissance Orbiter (LRO) and DEM data acquired by the Lunar Orbiter Laser Altimeter (LOLA). Experimental results demonstrate that integrating optical images with DEM data significantly decreases the number of false positives compared with using optical images alone, with F₁-score of 84.8% on average. Moreover, compared with other existing fusion methods, our proposed method was quite advantageous especially for the detection of small-scale craters with diameters less than 1000 m.     

Integrated method for crater detection from topography and optical images and the new PH9224GT catalogue of Phobos impact craters

In a large majority of lunar and planetary surface images, impact craters are the most abundant geological features. Therefore, it is not surprising that crater detection algorithms (CDAs) are one of the most studied subjects of image processing and analysis in lunar and planetary science. In this work we are proposing an Integrated CDA, consisting of: (1) utilization of DEM (digital elevation map)-based CDA; (2) utilization of an optical-based CDA; (3) re-projection of used datasets and crater coordinates from normal to rotated view and back; (4) correction of the brightness and contrast of a used optical image; and (5) tile generation for the optical-based CDA and an assembling of results with an elimination of multiple detections, in combination with a pyramid approach down to the resolution of the available DEM image; and (6) a final integration of the results of DEM-based and optical-based CDAs, including a removal of duplicates. The proposed CDA is applied to one specific asteroid-like body, the small Martian moon Phobos. The experimental evaluation of the proposed CDA is done by a manual verification of crater-candidates and a search for uncatalogued craters. The evaluation has shown that the proposed CDA was used successfully for cataloging Phobos craters. The major result of this paper is the PH9224GT – currently the most complete global catalogue of the 9224 Phobos craters. The possible applications of the new catalogue are: (1) age estimations for any selected location; and (2) comparison/evaluation of the different chronology and production functions for Phobos. This confirms the practical applicability of the new Integrated CDA – an additional result of this paper, which can be used in order to considerably extend the current crater catalogues.     

Detection of sub-kilometer craters in high resolution planetary images using shape and texture features

Counting craters is a paramount tool of planetary analysis because it provides relative dating of planetary surfaces. Dating surfaces with high spatial resolution requires counting a very large number of small, sub-kilometer size craters. Exhaustive manual surveys of such craters over extensive regions are impractical, sparking interest in designing crater detection algorithms (CDAs). As a part of our effort to design a CDA, which is robust and practical for planetary research analysis, we propose a crater detection approach that utilizes both shape and texture features to identify efficiently sub-kilometer craters in high resolution panchromatic images. First, a mathematical morphology-based shape analysis is used to identify regions in an image that may contain craters; only those regions - crater candidates - are the subject of further processing. Second, image texture features in combination with the boosting ensemble supervised learning algorithm are used to accurately classify previously identified candidates into craters and non-craters. The design of the proposed CDA is described and its performance is evaluated using a high resolution image of Mars for which sub-kilometer craters have been manually identified. The overall detection rate of the proposed CDA is 81%, the branching factor is 0.14, and the overall quality factor is 72%. This performance is a significant improvement over the previous CDA based exclusively on the shape features. The combination of performance level and computational efficiency offered by this CDA makes it attractive for practical application.     

Crater detection via genetic search methods to reduce image features

Recent approaches to crater detection have been inspired by face detection’s use of gray-scale texture features. Using gray-scale texture features for supervised machine learning crater detection algorithms provides better classification of craters in planetary images than previous methods. When using Haar features it is typical to generate thousands of numerical values from each candidate crater image. This magnitude of image features to extract and consider can spell disaster when the application is an entire planetary surface. One solution is to reduce the number of features extracted and considered in order to increase accuracy as well as speed. Feature subset selection provides the operational classifiers with a concise and denoised set of features by reducing irrelevant and redundant features. Feature subset selection is known to be NP-hard. To provide an efficient suboptimal solution, four genetic algorithms are proposed to use greedy selection, weighted random selection, and simulated annealing to distinguish discriminate features from indiscriminate features. Inspired by analysis regarding the relationship between subset size and accuracy, a squeezing algorithm is presented to shrink the genetic algorithm’s chromosome cardinality during the genetic iterations. A significant increase in the classification performance of a Bayesian classifier in crater detection using image texture features is observed.     

Hybrid method for crater detection based on topography reconstruction from optical images and the new LU78287GT catalogue of Lunar impact craters

Impact craters are ubiquitous and well-studied structures of high geological relevance on the surfaces of the Earth’s Moon, the terrestrial planets, the asteroids and the satellites of the outer planets. Therefore, it is not surprising that crater detection algorithms (CDAs) are one of the most studied subjects of image processing and analysis in lunar and planetary science. In this paper we are proposing a Hybrid CDA: a modified DEM (digital elevation map) reconstruction method used as a step in an existing CDA based on Hough transform. The new Hybrid CDA consists of: (1) reconstruction of topography from optical images using a shape from shading approach; (2) utilization of the DEM-based CDA; (3) correction of brightness and contrast of optical images used in order to be more suitable for evaluation of detections. An additional result of this work is a new method for evaluation of topography reconstruction algorithms, using a DEM-based CDA and an earlier approach for evaluation of CDAs. The new Hybrid CDA was tested using two Chandrayaan-1 Moon Mineralogy Mapper (M³) images and two excerpts of the Lunar Reconnaissance Orbiter (LRO) Wide Angle Camera (WAC) global optical image mosaic. As a result, the number of craters inside these four regions increased considerably from 1754 (as available in the previous LU60645GT catalogue) to 19 396 craters (as available in the resulting new LU78287GT catalogue). This confirmed the practical applicability of the new Hybrid CDA, which can be used in order to considerably extend current crater catalogues.     

基于深度学习的大型陨石坑识别方法研究

陨石坑是天体表面最为显著的地形特征，传统陨石坑识别方法主要是对小型陨石坑正负样本的二分类问题研究，且效率和精度均不高。以星体宏观视角下的大型陨石坑作为研究对象，结合图像处理和神经网络等方面的知识，创建了来自不同数据源的陨石坑样本数据库，研究了数据源对网络模型泛化能力的影响，提出了一种效率更高的陨石坑多分类识别方法。在非极大值抑制（NMS）算法基础上，提出了一种精度更高的陨石坑检测算法。经过参数优化和实验验证，构建的基于深度学习的多尺度多分类陨石坑自动识别网络框架取得了较高的准确率，在同源验证集上识别率可达0.985，在异源验证集上识别率可达0.863，并且有效改善了目标检测时检测框冗余及误检测的问题。      

一种基于多尺度边缘提取的陨石坑检测算法

针对星体表面的陨石坑可用于探测器的自主导航、障碍识别等任务,提出一种基于多尺度边缘提取的陨石坑检测算法。该算法首先利用高斯金字塔得到不同尺度的陨石坑图像;其次,针对不同尺度的陨石坑图像,利用EDPF边缘提取算法对陨石坑进行边缘提取,并连接关键边缘像素点为直线段来近似表示图像边缘;然后将具有相同偏转方向的边缘直线段连接成圆弧,并将有相似半径和中心的圆弧拟合成候选圆和椭圆;最后对候选圆、椭圆进行验证。该算法的优点在于,能够准确地检测出陨石坑,有较高的检测率,且对存在较多陨石坑的图像有较好的检测结果。     

着陆避障中双目立体匹配方法与仿真

摘要： 在着陆避障过程中，利用双目相机拍得的图片进行立体匹配得到稠密的地形高程图是一种有效的方式.文中对绝对误差之和(sum of absolute differences, SAD)、全Census变换以及稀疏Census变换立体匹配方法原理进行说明，针对SAD算法无法处理左右图像亮度不一致的情况提出了对匹配窗口进行灰度规范化以及加入LOG滤波的改进措施，并且对上述方法分别应用数据库图像和沙盘拍图进行仿真测试及比较，从匹配错误率和实时性的角度分析各算法在地外星体着陆这种特殊场景中的使用性能.     

一种基于自动特征学习的陨石坑区域检测算法

基于陨石坑的视觉导航技术成为一种新颖的高精度空间探测自主导航方式,如何从导航图像中精确地提取陨石坑区域是实现基于陨石坑视觉导航的首要条件。针对这一问题,根据陨石坑导航图像特点,提出了一种基于自动特征学习的陨石坑区域检测算法。首先,基于最大稳定极值区域检测算法提取陨石坑候选区域;其次,利用卷积神经网络（CNN）自动学习提取候选区域的特征;最后,通过支持向量机（SVM）实现候选区域的精确分类,得到真实的陨石坑区域。大量的仿真实验表明:与传统的基于人工特征的陨石坑区域检测算法相比,提出的基于自动特征学习的陨石坑区域检测算法具有更高的检测精度和更好的鲁棒性,在通用火星表面陨石坑数据集上,所提算法的F₁度量指标较于传统算法高出8%,可以广泛地应用于基于陨石坑的视觉导航算法中的陨石坑区域提取,为基于陨石坑视觉导航算法提供精确的导航路标输入。      

Open framework for objective evaluation of crater detection algorithms with first test-field subsystem based on MOLA data

Crater Detection Algorithms (CDAs) applications range from estimation of lunar/planetary surface age to autonomous landing on planets and asteroids and advanced statistical analyses. A large amount of work on CDAs has already been published. However, problems arise when evaluation results of some new CDA have to be compared with already published evaluation results. The problem is that different authors use different test-fields, different Ground-Truth (GT) catalogues, and even different methodologies for evaluation of their CDAs. Re-implementation of already published CDAs or its evaluation environment is a time-consuming and unpractical solution to this problem. In addition, implementation details are often insufficiently described in publications. As a result, there is a need in research community to develop a framework for objective evaluation of CDAs. A scientific question is how CDAs should be evaluated so that the results are easily and reliably comparable. In attempt to solve this issue we first analyzed previously published work on CDAs. In this paper, we propose a framework for solution of the problem of objective CDA evaluation. The framework includes: (1) a definition of the measure for differences between craters; (2) test-field topography based on the 1/64° MOLA data; (3) the GT catalogue wherein each of 17,582 craters is aligned with MOLA data and confirmed with catalogues by N.G. Barlow et al. and J.F. Rodionova et al.; (4) selection of methodology for training and testing; and (5) a Free-response Receiver Operating Characteristics (F-ROC) curves as a way to measure CDA performance. The handling of possible improvements of the framework in the future is additionally addressed as a part of discussion of results. Possible extensions with additional test-field subsystems based on visual images, data sets for other planets, evaluation methodologies for CDAs developed for different purposes than cataloguing of craters, are proposed as well. The goal of the proposed framework is to contribute to the research community by establishing guidelines for objective evaluation of CDAs.     

Exploring links between crater floor mineralogy and layered lunar crust

Floors of similar sized craters, representing material from similar depth horizons, have been studied to explore their suitability as mineralogy indicators at various depths within the lunar crust. Clementine UV–vis multispectral data was used to generate mineral abundance maps of crater floors and surroundings using a modified version of algorithm given by Pieters et al. (2001) [Pieters, C.M., Head, J.W., Gaddis, L., Jolliff, B. and Duke, M. Rock types of the south pole aitken basin and extent of basaltic volcanism, JGR (106) E11, 28001–28022, 2001.]. Substantial processing of the crater floor material due to variety of geological processes is evident in the generated maps, making straight forward interpretations difficult. However, systematic compositional trends in fresh craters on the floors of target craters seem to indicate the feasibility of such an effort.     

基于鲁棒曲线匹配的星表特征跟踪方法

针对星际着陆的特征跟踪及障碍识别过程,提出了一种基于尺度不变的曲线描述及匹配方法。该方法利用曲线邻域的梯度信息形成特征描述符,无需知道各点曲率或曲线之间的几何构型,且对旋转、缩放、视角变化及光照影响都具有较强的鲁棒性。仿真实验表明,该算法不仅能够对不同尺度的陨石坑、岩石边缘等障碍或自然陆标进行识别,而且能够实现边缘曲线特征的跟踪,误匹配率低。     

基于运动增强和颜色分布比对的运动目标检测

为检测无人机视频中的地面运动目标,提出了一种运动和颜色信息相结合的算法.采用前向运动历史图像来增强独立运动信息和抑制背景噪声,确保完整分割出候选运动区域; 提出一种迭代的、基于局部颜色分布比对的方法,去除候选区域中的背景像素,以更准确地提取单个运动目标.算法不仅节约了计算量,还有效降低了误检和漏检的可能性.多组无人机视频的实验结果表明了所提算法的高效性和鲁棒性.     

一种小行星探测陆标导航方法

针对小行星探测下降着陆段精确相对导航问题,提出了一种基于Stereophotoclinometry (SPC)陆标的相对导航方法.该方法先采用SPC光照模型渲染陆标的预测图像,再利用双线性插值从观测图像中生成陆标的提取图像.通过组合相关匹配实现预测图像与提取图像的高精度匹配,得到陆标中心点对应观测图像位置,即可利用N点透视(PNP)算法进行相机位姿估计.仿真结果表明,该方法在初始相机位姿偏差较大时也能达到亚像素的图像位置确定精度,并且具有较好的实时性.     

Hydrocode modelling of hypervelocity impacts on ice

Experimental data are now widely available for the size of craters resulting from hypervelocity impacts of millimetre scale projectiles onto water ice targets. At such size scales the bowl shaped crater formed in ductile materials, or in larger scale impacts, is here surrounded by a large spallation zone due to the brittle nature of the ice. Modelling of these impacts therefore has to take account of this spallation. Here we used the iSALE2 hydrocode to simulate such impacts and compared the results to experimental data. We found that it was possible to reproduce the experimental data over a range of speeds (1–7 km s⁻¹) for aluminium and copper projectiles. Initially, to reproduce the large spallation regions around the craters it was assumed that above a certain degree of damage, material was removed by spallation. However this simple one-parameter model failed to model the crater depth adequately. Accordingly, to obtain the best agreement of the simulations with the experimental data, a two-step ice strength was introduced, whereby above a critical amount of damage (0.95), the yield strength reduced from 1 MPa (intact) to 70 kPa (damaged). As a result, experimental data for crater depth and diameter and the results of the simulations agree to within 6% for diameter and 5% for depth over the impact energy range used in the experiments (1–240 J).     

利用数字高程模型自动检测火星表面陨石坑

为了克服利用影像识别陨石坑的诸多限制因素,利用"火星全球勘探者"(MGS)火星激光高度计(MOLA)得到的火星三维DEM数据,转换获得地形曲率,然后利用设定阈值将曲率图转换为二值图像,结合图像分割floodin算法可以得到待检测陨石坑,最后利用Hough变换可以检测出陨石坑。其成功率达到73.4%,可以有效地从DEM中识别陨石坑。利用DEM识别陨石坑的方法可以识别更多新的陨石坑,为现存的陨石坑目录提供新的数据信息。     

The first new application of the Mathematical Theory of Stochastic Processes to lunar and planetary science: Topography-Profile Diagrams of Mars

The Mathematical Statistics Theory (MST) and the Mathematical Theory of Stochastic Processes (MTSP) are different branches of the more general Mathematical Probability Theory (MPT) that can be used to investigate physical processes through mathematics. Each model of a stochastic process, according to MTSP, can provide one or more interpretations in the MST domain. A large body of work on impact crater statistics according to MST exists, showing cumulative crater frequency (N km⁻²) as a function of age (years) for some particular crater diameter. However, this is only one possible representation in the MST domain of the bombardment of the planetary surface modeled as a stochastic process according to MTSP. The idea that other representations are possible in the MST domain of the same stochastic process from MTSP has been recently presented. The importance of the approach is that each such mathematical-based interpretation can provide a large amount of new information. Coupled with MOLA data, Topography-Profile Diagrams (TPD) are one of the many examples that can provide a large amount of new information regarding the history of Mars. TPD consists of: (1) Topography-Profile Curve (TPC), which is a representation of the planet’s topography, (2) Density-of-Craters Curve (DCC), which represents density of craters, (3) Filtered-DCC (FDCC), which represents DCC filtered by a low-pass filter, included with the purpose of reducing the noise, and (4) Level-of-Substance-Over-Time Curve (LSOTC), which represents interpretation of the influence on the distribution of craters shown by FDCC. TPC uniquely corresponds to the computation of TPD, whereas DCC depends on algorithms for computing the elevation of each crater according to the topography, center coordinates, and radius of impact crater, and FDCC relies on the architecture of the custom designed low-pass filter for filtering DCC. However, all variations of DCC and FDCC, which includes the various impact crater data sets, showed a correlation among the density of craters and elevation over 70–80% of the planet surface. Additionally, if we assume that the ocean primarily caused the noted correlation, LSOTC offers a mathematical approach for estimating topographic change of the ocean’s extent over time. Accordingly, TPD is the first new practical application of MTSP to lunar and planetary sciences, showing correlation of topography to a physical process.