Crater Marking and Classification Using Computer Vision

Abstract

Abstract. In the last three years NASA and some other Space Agencies have draw some interest to date Mars surface, mainly because the relationship between its geological age and the probable presence of water beneath it. One way to do this is by classifying craters on the surface attending to their degree of erosion. The naïve way to solve this problem would let a group of experts analyze the images of the surface and let them mark and classify the craters. Unfortunately, this solution is unfeasible because the number of images is huge in comparison with the human resources any group can afford. Different solutions have been tried [1], [2] over this period of time. This paper offers an autonomous Computer Vision System to detect the craters, and classify them.

Circular Degree Hough Transform

“…The selected values not fulfilling these requirements were qualified as a FR or a FA. For the detection, the edges deletion of selected circular shapes was done as proposed in [13]. Some results are included in Figure 4.…”

Moon Impact Crater Detection Using Nested Attention Mechanism Based UNet++

“…Many researchers have also proposed a series of lunar surface impact craters extraction algorithms. According to the different design ideas of these algorithms, their development can be roughly divided into two directions: the traditional algorithm that uses image processing technology to identify impact craters [5,6] and the intelligent algorithm that introduces a deep learning model to extract impact craters [7][8][9][10].…”

“…The accuracy of the craters depends on the judge reader's prior knowledge, and the recognition efficiency is low, so many reliable methods are created to automatically extract the impact crater. Among them, the methods based on morphology fitting include Hough transform [6,11], conic curve fitting [12], template matching [13], and other algorithms [14]. Bue [6] and Michael [11] used Hough transform to analyze and process MOLA data, and the latter obtained more than 75% impact craters with a diameter higher than 10 km.…”

“…Among them, the methods based on morphology fitting include Hough transform [6,11], conic curve fitting [12], template matching [13], and other algorithms [14]. Bue [6] and Michael [11] used Hough transform to analyze and process MOLA data, and the latter obtained more than 75% impact craters with a diameter higher than 10 km. The advantage of fuzzy Hough transform is to search the data point set with a high fitting degree, which can effectively reduce the error boundary obtained by traditional Hough transform.…”

Enhancing Impact Crater Contours to Increase Recognition Rates

“…The identification and counting of impact craters is an approach that has been widely used when establishing the chronology of planetary surfaces (Hartmann and Neukum, 2001). The early manual crater counts on optical images can now be aided by several semi-automatic approaches from the image processing and pattern recognition fields -Homma et al (1997), Honda and Azuma (2000), Leroy et al (2001), Costantini et al (2002), Vinogradova et al (2002), Michael (2003), Flores-Méndez (2003), Kim and Muller (2003), Brumby et al (2003), Magee et al (2003), Plesko et al (2004), Barata et al (2004), Kim et al, (2004), Earl et al (2005) and Matsumoto et al (2005) -but the generalization of procedures still meets with evident difficulties. Even in a recent study (Neukum et al, 2004) in which a refinement of chronology was proposed for a number of small areas of the surface of Mars, automatic recognitions were not fully trusted, and ended up being edited and manually corrected by human experts.…”