**6. Comparison with other previous detection method**

In comparison to other techniques in terms of performance, this craters detection algorithm also has an understanding performance in terms of accuracy measurement to the previous algorithm proposed by Sawabe, Matsunaga and Rokugawa in 2005. As highlighted and calculated above, it is proven that the craters detection algorithm has an accuracy detection of 77% based on the two images tested above. This understanding percentage measurement is based on how much craters are detected compared with the pre-processed image as in Figures (14) and (15) above. The detected craters are measured from groups of pairing patches (light and dark) with minimum distances and angles detection.

This proposed craters detection algorithm by the authors can be improved by introducing more approaches like edge detections of each crater and evaluating more techniques from the morphological image analysis. To experiment more with the image morphology, the authors have tested the edge detection method using *prewitt* and *canny* on the original Image 2 using the algorithm and the results are shown in Figures (22) and (23) below. As can be seen b, *canny* method has detected the edges more precisely than the *prewitt* method. There are some previous craters detection algorithms implemented in this edge detection method as proposed by Yang Cheng and Adnan Ansar in their proposed craters detection technique [5]. Edge detections are usually used in a pre-processing step to obtain better results before the shape of the crater can be detected.

**Figure 20.** Edge detections using 'prewitt' detector

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

angle (not too low and too high) and a low noise image is a bonus.

Therefore, the image has to be taken by the spacecraft's camera under ideal sun elevation

**Figure 19.** Low detection of craters by the algorithm because of too many unnecessary blobs (tiny

independent of the shape detection whether it is a circle or an ellipse.

**6. Comparison with other previous detection method** 

patches (light and dark) with minimum distances and angles detection.

However, for the advantages, the algorithm itself can detect the craters without knowing the main parameters such as the size (radius/diameter or the gradient of the craters). It is an uncomplicated detection algorithm and has a fast detection performance. Under a clear image (low noise, good lightning condition and ideal sun elevation angle) where the pattern is easily distinguishable, the accuracy will be much higher. Besides, the craters detection is

In comparison to other techniques in terms of performance, this craters detection algorithm also has an understanding performance in terms of accuracy measurement to the previous algorithm proposed by Sawabe, Matsunaga and Rokugawa in 2005. As highlighted and calculated above, it is proven that the craters detection algorithm has an accuracy detection of 77% based on the two images tested above. This understanding percentage measurement is based on how much craters are detected compared with the pre-processed image as in Figures (14) and (15) above. The detected craters are measured from groups of pairing

This proposed craters detection algorithm by the authors can be improved by introducing more approaches like edge detections of each crater and evaluating more techniques from

blobs)

In comparison to the multiple approaches craters detection and automate craters classification algorithm proposed by Sawabe, Matsunaga and Rokugawa in 2005, the algorithm has an accuracy of 80%. Four approaches are implemented in the craters detection algorithm to find shady and sunny patters in images with low sun angles, circular features in edge images, curves and circles in thinned and connected edge lines and discrete or broken circular edge lines using fuzzy Hough transform. In this particular research, they have considered a crater as a circle and used circular Hough transform to detect circular feature of a crater. The detected crater is then classified by spectral characteristics derived from Clementine UV-Vis multi-spectral images. Although it has more percentage of accuracy compared with the algorithm proposed by the authors, it has a limitation such as the crater has to be assumed to be a circle before it can be used to detect a crater. If the authors have an ellipse in the image, then it will be difficult to use this method of detection.

**Figure 21.** Edge Detection using 'canny' detector

Previously, there were quite a number of craters detection algorithms using Hough Transform especially using circular features detection as proposed by E.Johnson, A.Huertas, A.Werner and F.Montgomery in their paper [7]. As emphasized above, a camera will capture an ellipse if the image is taken from a certain angle and certain distance relative to the moon's surface. An ellipse will have five dimensions that have to be considered in the Hough algorithm when detecting shapes. An ellipse is more complicated to be detected than a circle because a circle just has 3 dimensions to be considered. It will certainly have a complex codes hence will take a longer time to construct. That is the reason why the authors have created an uncomplicated and robust algorithm in detecting hazards mainly craters on the moon's surface for easy implementation.
