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Experimental results show that image enhancement is necessary for a better perfor- mance of the BSM, as the number of MSC from the shadow segmented enhanced images is higher compared to the number of MSC obtained from the original NAC images. In terms of individual performance, HE and AHE provide the highest per- centage of MSC compared to CLAHE and ESIHE. The performance of AHE is good compared to other methods in terms of percentage of MSC points. The disadvantage of using AHE and HE is the number of USC points. The percentage of USC is very high in case of HE and AHE and the effect of high USC points on BSM needs to be checked.

The comparison of the enhancement methods with respect to the incidence angle shows that HE and AHE are dominating compared to CLAHE and ESIHE. But the performance of CLAHE and ESIHE increases with higher incidence angle. The analysis of the performance of enhancement algorithms with respect to the surface reflectance shows that the performance of AHE and HE are good for high or low reflectance regions. Whereas for regions combined with high and low reflectance surface the percentage performance of CLAHE and ESIHE increases. This supports the idea of selecting CLAHE and ESIHE as the suitable candidate for image enhance- ment of high dynamic range images (images with bright and dark region). It can also be concluded from these results that different enhancement algorithms are required for different images and hence an automated classification pipeline is required. As discussed in chapter: 5 the major issue faced during the experiment was the spatial shift in the rendered images. The images rendered using the DTM models had spatial shift in both latitude and longitude compared to the corresponding NAC images. Due to the manual correction process required to correct the shift, the database available for testing was largely reduced.

Two methods were used to create the classification logic. In method one the parameter values were assigned a weight, based on the standard deviation, which was calculated from the tests with a training data set. A classification logic was developed using the combined weighted parameter values. Using this classification logic 28.26% of the images were enhanced with the correct method. The second method to design the classifier was to use the multi linear regression method, where each of the methods were assigned a numeric value and used as dependent variable. The parameter values were used as independent variable, and the obtained coefficients were used on the test data set. For 50% of the images, the selected enhancement method were correct when the classifier based on the multi linear regression was used. Both methods are below the desired 90% desired classifier performance.

AHE were best methods are similar and hence the classifier selects HE and AHE interchangeably. Another reason for the lag in the performance of the classifier was identified to be the lack of data. For the training of enhancement methods CLAHE and ESIHE only 2 images were available, which is not enough to decide the confidence or fit the model using multi-linear regression.

fulfilled. The next section gives an overview of the future improvements, by proposing possible solutions for the issues faced during the thesis, and the last section gives the conclusion.

7.1

Thesis summary

The BSM proposed by Kaufmann et al. [18] works on the principle of using shadows on the lunar surface for navigation. The shadows are identified as features and the pose of the landing spacecraft is estimated by comparing these features between a reference image and a real image. To test the concept Kaufmann has used rendered image for both, reference and descent image. As a next step it was necessary to test the BSM system with real lunar images. A preliminary analysis of real lunar images suggested that image enhancement is necessary to increase the number of segmented shadows.

Based on the preliminary analysis, two objectives were identified. The first objective was to look for an enhancement method, which is applicable on all the images. In case, one such algorithm is not available, the second objective was to set up an automatic pipeline for choosing the best enhancement method. The purpose of the pipeline is to classify an image based on visual information and parametric values, and enhance the image with the applicable enhancement algorithm.

For this purpose, data from the LRO mission was selected due to high resolution 2D NAC images and large amount of DTMs available for reference image rendering. As the performance of one enhancement method might not be suitable for all the images, as a first step NAC images were classified based on surface reflectance, topography and surface feature. The major factor affecting the enhancement method was considered to be the surface reflectance, as in the low reflectance region the surface can be misclassified as shadow, due to similar grey level values registered by the camera sensor. Various terrain and surface features such as craters mountains, rilles responsible for shadow generation on moon were also identified.

An image enhancement pipeline was implemented to classify and enhance the images automatically. The first stage of classification was to visually classify the images based on the scene information in the images, this is assumed to be prior mission knowledge. In the next stage the images were classified based on incidence angle1 as the variation in illumination level affects the dynamic range of the images. Hence, the applied enhancement method might be different. Further the images were classified objectively, based on classification parameter values. The performance of the enhancement algorithms was tested by comparing the shadows segmented

1_{for a mission the incidence angle is known as the time of landing on the lunar surface is decided}

from the enhanced NAC image with the shadows segmented from the rendered image. The selected enhancement algorithms were Histogram Equalisation (HE), Adaptive Histogram Equalisation (AHE), Contrast Limited Adaptive Histogram Equalisation (CLAHE), Exposure based Sub-Image Histogram Equalisation (ESIHE) and Homomorphic Filtering (HF).

The results show that, the enhancement of the real images is necessary. The number of the matched shadows between the rendered and the real images increases due to the applied enhancement. The most suitable image enhancement algorithms were the AHE and the HE, followed by the ESIHE and the CLAHE. The performance of the classifier was not as desired, but using multi-linear regression gives promising result for a combination of HE and AHE enhancement methods.