Aims and Objectives

The main focus of this study was to develop and design an image classification, storage and retrieval system for a CubeSat. This objective was achieved by providing an image classification, storage and retrieval system for a CubeSat that is able to classify images according to date and time, colour, shape and texture.

In terms of the stated sub-objectives:

7.2.1 To study and analyze space and Earth images and study methods to identify specific features.

The literature review on how to extract relevant features from an image were studied in Chapter Two. Techniques according to colour feature, shape feature and texture feature were investigated in order to identify features in an image. Identified Colour feature techniques included Colour Histograms, Colour Coherence Vector, Colour Correlograms and Colour Moments, and were studied in detail. Shape feature techniques such as boundary and region based shapes and texture feature techniques for instance Wavelet Transform, Gabor Wavelet, Tamura Feature, GLCM and LBP were studied.

7.2.2 To select and propose appropriate image processing techniques to process and extract features in an image.

From the detailed literature reviews, a suitable image processing technique to process and extract feature in an image were put forward. The Colour Histogram was chosen for colour features as it is a a global statistical feature that symbolizes the distribution of colours in an image. Colour histogram is robust, fast, require low storage and is easy to implement.

130 For the shape features, the chosen method was Mathematical Morphology, as it is . used to investigate the interaction between an image and a certain chosen structuring element by using the basic operations of erosion and dilation. This basic operation assists to identify objects which can be contained in an image. Shape features are vital in content based image retrieval, as they satisfy the requirement that two similar shapes in different images can be compared as they tend to be close or identical to one another’s values.

Within the texture feature the chosen method is GLCM which computes features such as Contrast, Energy, Correlation, and Homogeneity of gray level integer values. The contrast computes a total of local variations present in an image while energy is the sum of squared elements in GLCM. The homogeneity are diagonal distribution of elements in GLCM. Lastly, correlation illustrates how associated a pixel is to its neighbour in the image. Using this features an image can be compared and given a sample image the system will be able to identify what group/classes it might belong to. As GLCM rely on distance and direction of pixels in an image and four directions were used.

7.2.3 To design and propose an image classification scheme suited for CubeSat images.

A design and appropriate image classification techniques suitable for satellite images was designed in Chapter Five. The proposed design has broadly five stages namely input image stage, feature extraction stage, compare, rank and display stage and database storage stage. In the input stage an appropriate image is chosen from a list of images. Image noise is then removed using median filter method and image quality is improved. Feature extraction stage is whereby all suitable features are extracted from the image using Colour, Shape, Texture, Date and Time extraction methods. In the comparison stage, rank and display stage comparison is made using the distance measure of a query and stored images within the database. When values from the feature extraction method are used and are close to one another, images can be retrieved, ranked and displayed. The database storage stage: this is where all features extracted from an image are kept.

7.2.4 To classify images using image classification techniques (i.e., texture classification), once received from 3U CubeSat to ground station.

Upon completion of 3U CubeSat to be launched into the desired orbit, it is satellite images will be downlinked and received on the built bellville ground station, where images will be store in the database. However, before images can be stored, appropriate feature extraction techniques will be used to compute, classify and store

131 features in the database. Image features will be stored according to the date and time it were taken. The colour histogram and colour occurrence frequency pixels value will be computed and stored in the table called color-feature-table. Shape mathematical morphology parameters such as area, perimeter and metric will be computed so that an object in an image can be compared. Texture techniques parameters for instance as energy, correlation, homogeneity and contrast values will be computed and used to classify similar images which may belong in the same group.

7.2.5 To design a storage and retrieval system

The objective of designing a storage and retrieval system was achieved in Chapter Six. In Section 5.4.7 and Section 6.4 a database was designed and different tables were created to store various computed features being extracted from the images.

7.2.6 To study how images can be ranked and retrieved from a large database

In the proposed design discussed in Chapter Five, distance measure techniques were studied in Chapter Two (Section 2.7) and used to compare two images. The query image are compared against images in the database, and matches was obtained. To compute the ranking of the retrieved images, the distance measure values are used to find maximum or minimum values relating to each image. Sorting algorithms are then used to swap the highest value to lowest value.

7.2.7 To study techniques and evaluate the performance of an image retrieval system

In image retrieval systems, the performance and evaluation are vital for checking efficiency and effectiveness of the system. In this project, the precision and recall techniques were studied and used to evaluate the proposed system as discussed in Chapter Six (Section 6.6). Each proposed algorithm namely colour, shape, texture and date classifier were tested, evaluated and compared to other systems algorithms. The integrated proposed system when compared to other systems outperforms with an average precision of 100%. The proposed system is based on classifying, storing and retrieving of satellite images and can be used in high resolution applications.

7.2.8 To test, implement and integrate the proposed system for the CubeSat environment

Through experiments carried out in Chapter Six, the proposed design (Section 5.4) was implemented and tested (see Section 6.1 to Section 6.6). Each unit component of the proposed design was build and and different software tools were constructed in order to test functionality of the feature extraction, storing and retrieval of the system.

132 The integrated proposed system was implemented, tested and evaluated in Section 6.6.1(d).