Solar images are one of the most important sources of available information on the current state and behaviour of the sun, and the PICARD satellite is one of several ground and space-based observatories dedicated to the collection of that data. The PICARD satellite hosts the Solar Diameter Imager and Surface Mapper (SODISM), a telescope aimed at continuously monitoring the Sun. It has generated a huge cache of images and other data that can be analysed and interpreted to improve the monitoring of features, such as sunspots and the prediction and diagnosis of solar activity. In proportion to the available raw material, the little-published analysis of SODISM data has provided the impetus for this study, specifically a novel method of contributing to the development of a system to enhance, detect and segment sunspots using new hybrid methods. This research aims to yield an improved understanding of SODISM data by providing novel methods to tabulate a sunspot and filling factor (FF) catalogue, which will be useful for future forecasting activities. The developed technologies and the findings achieved in this research will work as a corner stone to enhance the accuracy of sunspot segmentation; create efficient filling factor catalogue systems, and enhance our understanding of SODISM image enhancement. The results achieved can be summarised as follows: i) Novel enhancement method for SODISM images. ii) New efficient methods to segment dark regions and detect sunspots. iii) Novel catalogue for filling factor including the number, size and sunspot location. v) Novel statistical method to summarise FFs catalogue. Image processing and partitioning techniques are used in this work; these methods have been applied to remove noise and detect sunspots and will provide more information such as sunspot numbers, size and filling factor. The performance of the model is compared to the fillers extracted from other satellites, such as SOHO. Also, the results were compared with the NOAA catalogue and achieved a precision of 98%. Performance measurement is also introduced and applied to verify results and evaluate proposal methods. Algorithms, implementation, results and future work have been explained in this thesis.