Identifying Lunar Craters using Texture

Impact craters are the geologic structures formed by the collision of meteoroids, asteroids or comets with planetary surfaces. Craters are common features on the surface of planetary bodies such as earth, moon etc. in the Solar System. In Moon, Mercury, or Mars we can see abundant of craters. Now-a-days many missions are launched to unknown planets to know about the life on planetary surface. The craters are studied more because craters are vital feature to estimate the age of the planetary surface. To detect craters manually is difficult and time consuming task. As there is a large volume of data from different satellite images and extracting efficient information from every image is a difficult task. There are different automatic and semiautomatic techniques to overcome this problems. In this survey I am going to discuss about the different techniques used for the crater detection and compare the efficiency of the techniques

2004

This paper presents a methodology to automatically recognise impact craters on the surface of Mars. It consists of three main phases: in the first one the images are segmented through a PCA of statistical texture measures, followed by the enhancement of the selected contours; in a second phase craters are recognised through a template matching approach; in a third phase the rims of the plotted craters are locally fitted through the watershed transform.

Planetary and Space Science, 2009

Impact craters are among the most studied geomorphic planetary features because they yield information about the past geological processes and provide a tool for measuring relative ages of observed geologic formations. Surveying impact craters is an important task which traditionally has being achieved by means of visual inspection of images. The shear number of smaller crater present in high resolution images makes visual counting of such craters impractical. In this paper we present a method that brings together a novel, efficient crater identification algorithm with a data processing pipeline; together they enable a fully automatic detection of sub-km craters in large panchromatic images. The technical details of the method are described and its performance is evaluated using a large, 12.5 m/pixel image centered on the Nanedi Valles on Mars. The detection percentage of the method is ∼70%. The system detects over 35,000 craters in this image; average crater density is 0.5 craters/km 2 , but localized spots of much higher crater density are present. The method is designed to produce "million craters" global catalogs of sub-km craters on Mars and other planets wherever high resolution images are available. Such catalogs could be utilized for deriving high spatial resolution and high temporal precision stratigraphy on regional or even planetary scale.

Current Science

This communication presents a framework for automatically classifying a crater image into one of its preservation states namely fresh, floor-fractured and degraded introducing a class of algorithms known as crater classification algorithms (CCA). This study involves identification of discriminatory parameters of classes, development and implementation of algorithms to automatically evaluate the parameters from a given Digital Elevation Model testing on representative craters of each class and evolve a decision tree framework for automatically classifying given crater image into its preservation class. This classification can be applied to craters that exhibit ambiguous topographies to test whether they were formed by impact erosion or igneous modification.

MATLAB - A Fundamental Tool for Scientific Computing and Engineering Applications - Volume 1, 2012

2004

Impact craters represent a novel and fundamental field of research in Solar System studies. The quest for Earth impact craters entails the exploitation of Earth Observation products and of their processing for the identification of crater features, their detection and possible recognition. In the past decades, impacts by extraterrestrial bodies were regarded as an interesting but certainly not important phenomenon in the spectrum of geological processes affecting the dynamic evolution of the Earth. On the contrary, at the present, the attention devoted to this phenomenon is significantly increased, thanks to the important role played by planetary exploration. According to planetary scientists [1], impact cratering was a dominant geological process during the growth of the planetary bodies of the Solar System. The emerging of this kind of view of planets as geological objects, places the search for unknown impact craters as a basic element for the study of the evolution of the planets, and in particular of the Earth. In this context, this paper addresses the issue of recognition and detection of impact craters on the Earth by using Earth Observation products (i.e. remote sensing images). In particular, approaches based on the Hough Transform and on the Radial Consistency measure are considered and compared. In addition, novel techniques (based on the use of the K-means algorithm and of stochastic processes) for the reduction of the computational load associated with the considered approaches are described. Preliminary experimental results are reported that point out advantages and disadvantages of the considered methods.

Journal of Volcanology and Geothermal Research, 2020

The morphologies of craters on planetary surfaces reveal clues about the geologic mechanisms by which they originate and subsequently evolve, as well as the materials and physical variables inherent to the environment in which they formed. We carried out a quantitative multivariate analysis of shape descriptors derived from the outlines of craters formed by volcanic processes on Mars, Io, and Earth and by impact cratering on the Moon using elliptic Fourier analysis (EFA) and the Zahn-Roskies (Z-R) shape function. Canonical variate analysis (CVA) was used to construct a statistical model of differences between the crater groups to classify craters produced by various volcanic and impact processes. The classification model from canonical variate analysis of EFA shape descriptors yielded a 90% rate of success for the assignment of group membership among 406 examined craters. It correctly classified 138 of 154 (90%) ionian paterae,154 of 155 (99%) lunar impact craters, 31 of 35 (89%) terrestrial basaltic shield calderas, 32 of 38 (84%) terrestrial ash-flow calderas, and 12 of 24 (50%) martian basaltic shield calderas. The classification model from canonical variate analysis of Z-R shape function descriptors classified 84% of the total population of the examined craters correctly. The analysis correctly classified 96% of ionian paterae, 100% lunar impact craters, 51% terrestrial basaltic shield calderas, and 63% martian calderas, but only 16% of the terrestrial ash-flow calderas were correctly classified. Canonical variate analysis of EFA and Z-R results shows that the shapes of ash-flow calderas and paterae on Io differ the least of all groups included in this study, and basaltic shield calderas and martian calderas analyzed together also have few differences. The Z-R model successfully classifies more ionian patera and impact craters than the EFA classification model but performs poorly at classifying the other crater groups. This result shows that the descriptors convey different shape information. The Z-R model is robust in its ability to classify endmember differences in complexity while the EFA model is robust in its ability to reliably classify among more groups. These differences and similarities in shape confirm previously understood commonalities related to the origin and evolution of various types of craters. In general, basalt shield calderas on Earth and Mars are morphologically similar and are thought to have similar origins; this study confirms that the 2-D shapes of their craters are quantitatively correlated. Similarities have been noted between terrestrial ash-flow calderas and paterae on Io, principally in their large sizes, shallow magma chambers and complex evolution; this study confirms their shapes are also similar. Impact craters and ionian paterae are most dissimilar, as are their evolutions. This study demonstrates rigorous landform shape analysis can greatly increase our understanding of the diversity in craters and the processes involved in their formation.

Planetary and Space Science, 2012

Impact craters are some of the most abundant geological features on most lunar and planetary bodies, providing insight into the history and physical processes that shaped their surface. It is therefore not surprising that extensive research has been done in the past on laboratory craters, as well as on crater detection algorithms (CDAs). No prior work has investigated how CDAs can assist in the research of laboratory craters, nor has an alternative formal method for evaluation of the similarity between laboratory and real impact craters been proposed. The result of this work is a test-field for evaluation of laboratory craters which includes: (1) a procedure for creation of explosion-induced laboratory craters in stone powder surfaces; (2) a method for 3D scanning of laboratory craters using a GOM-ATOS-I 3D scanner; (3) a new method for emplacement of laboratory craters into the topography of a planetary body; (4) a new method for objective evaluation of laboratory craters which utilizes the CDA, the Turing test, and a new measure of similarity between laboratory and real craters; and (5) a possibility of accompanying manual evaluation of laboratory craters using 2D topographical profiles. The successful verification of the proposed test-field, using Martian and Lunar global DEMs and local high-resolution DEMs, confirmed possibilities for the proposed scientific investigations of real impact craters using laboratory craters as proxies. This cost-effective approach also promises affordable accessibility for introductory physics and astronomy laboratories.

IRJET, 2020

The study shows various attempts made on detection of craters and its segmentation based on sizes and several features. We have studied some of the previous work done in the field of crater detection using deep learning approaches and presented a literature survey considering some of the most recognized work over the globe. This study is presented in order to get a quick glimpse of existing work in the field for a researcher to validate and try different approaches.

2020

Impact crater identification and localization are vital to age estimation studies of all planetary surfaces. This study illustrates the utility of object detection based approach for impact crater identification. We demonstrate this using the You Only Look Once (YOLO) object detection technique to localize and identify impact craters on the imagery from Context Camera onboard the Mars Reconnaissance Orbiter. The model was trained on a chipped and augmented dataset of 1450 images for 13000 iterations and tested on 750 images. Accuracy and sensitivity studies reveal a Mean Average Precision of 78.35%, precision of 0.81, recall (sensitivity) of 0.76 and F1 score of 0.78. We conclude that object detection techniques such as YOLO may be employed for crater counting studies and database generation. INTRODUCTION Cumulative crater size-frequency distribution studies have been widely used in establishing the age of planetary landforms [1,2]. The crater identification process for such studies...