Image processing techniques are essential part of the current computer technologies and that it plays vital role in various applications like medical field, object detection, video surveillance system, computer vision etc. The important process of Image processing is Image Segmentation. Image Segmentation is the process of splitting the images into various tiny parts called segments. Image processing makes to simplify the image representation in order to analyze the images. So many algorithms are developed for segmenting images, based on the certain feature of the pixel. In this paper different algorithms of segmentation can be reviewed, analyzed and finally list out the comparison for all the algorithms. This comparison study is useful for increasing accuracy and performance of segmentation methods in various image processing domains.

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Image Registration (IR) is the process of transformation of different data into the coordinate system and provides the geometric alignment of two images used in the computer vision, medical imaging and remote sensing applications. An image registration is an important stage in multi-temporal image processing since, the recovery of information from cloud shadow is difficult. Traditionally, the Demons, Combined Registration and Segmentation (CRS) approach, Markov Random Field (MRF) and Mutual Information (MI) based approaches offers more computational complexity, minimum edge preservation measure (QAB/F) during image registration process. To maximize the quality of edge preservation measure and MI with minimum computational time, this paper proposes hybrid Particle Swarm Optimization (PSO)-Affine Transformation (AT) technique for an image registration. An enhanced registration process and the cloud removal technique are proposed for quality improvement of an image. Initially, Gaussian filtering in the preprocessing stage removes the noises present in an image. The proposed PSO extracts the matching points between the reference image and target image in the multitemporal image dataset. Then, the AT on extracted matching points provides the specific feature points from main features. Finally, the Relevance Vector Machine (RVM) classification forms the cluster of specific feature points. The extracted feature points from PSO-AT maximize the quality of edge preservation and MI with efficient cloud removal. The comparative analysis with the traditional methods of Control Point -Least Square (CP-LS), MultiFocus Image Fusion (MFIF) and Discrete Wavelet Transform (DWT) on the parameters of QAB/F and MI shows the effectiveness of proposed PSO-AT.