**2. Methodology**

488 MATLAB – A Fundamental Tool for Scientific Computing and Engineering Applications – Volume 1

determination of accuracy will be evaluated in the experimental results afterwards.

(craters).

In Landmark Based Pinpoint Landing Simulator (LAMPS) by Cheng and Ansar, a robust yet complex crater detection algorithm has been developed for autonomous spacecraft navigation. Based on their research, craters might have a random appearance based on their ages and sizes. For example, younger craters may have sharper and regular rims [14]. Spatial densities of craters also form the primary basis for assessing the relative and absolute ages of geological units on planetary surfaces [14]. However, typical craters will have ellipse shape in their rims, with a light to dark pattern that is dictated by the sun azimuth and elevation as well as its own topography. In fact, this statement is very similar to the authors' own approach in defining a crater as a composition of light and dark patch. Technically, Cheng and Ansar approach algorithm consists of five major steps which are edge detection, rim edge grouping, ellipse fitting, precision fitting and crater confidence evaluation. Another important property of landmark based detection system is the use of spacecraft pinpoint landing (PPL) for autonomous navigation method. To decrease the probability of landing on a hazard surface, one of the two safe landing proposals must be taken into account: craters hazard detection avoidance, which will detect all hazardous craters upon landing on the moon's surface or pinpoint landing which determines the Lander's position in real time and guide the spacecraft to a safe and free landing site, away from those hazards

According to recent studies on the size and frequency of the craters on a Mars' surface [17], a sufficient number of adequately sized craters for determining spacecraft position are very likely to be found in descent imagery. For an instance, if the image was taken using a camera field of 45 degrees and is taken from 8km above the surface, there will be an average of 94 craters of less than 200m in diameter. Ideally, from this situation, these craters can be

There is also multiple approach algorithms in detecting craters on the lunar's surface as proposed by Sawabe, Matsunaga and Rokugawa, 2005. It is known that the crater's feature changes according to its size. Small craters form a simple circle, and the larger its size, the more complex its shape becomes [3]. This change in feature poses difficult problems to detect craters with different sizes by a single approach. In their data-dependant based algorithm, they defined that a crater is a circular topographical feature in images and a minimum detection crater size is two pixels in radius [13] and it uses data from SELENE (Selenological and Engineering Explorer) to visualize the surface geological settings and the subsurface structure of the Lunar. These approaches are different to the authors' research as they consider the crater to bean ellipse for their detection algorithm. The authors also propose the data independent based algorithm. Four different methods were used with the crater detecting algorithm to find (1) 'shady and sunny' patterns in images with low sun angle, (2) circular features in edge images (3)curves and circles in thinned and connected edge lines, and (4)discrete or broken circular edge lines using fuzzy Hough transform. Besides, the detected craters are also classified by spectral characteristics derived from Clementine UV-Vis multi-spectral images [13]. The main advantages of the proposed algorithm compared to the previous one are that the detection algorithm is uncomplicated and it has an outstanding successful rate of detections. These methods of detection and their
