With the advancement of technology and the development of more sophisticated remote sensing sensor systems, the use of satellite imagery has opened up various fields of exploration and application. There has been an increased interest in analysis of multi-temporal satellite image in the past few years because of the wide variety of possible applications of in both short-term and long-term image analysis. The type of changes that might be of interest can range from short-term phenomena such as flood assessment and crop growth stage, to long-term phenomena such as urban fringe development. This thesis studies flood assessment and land cover mapping of satellite images, and proposes nature inspired algorithms that can be easily implemented in realistic scenarios. Disaster monitoring using space technology is one of the key areas of research with vast potential; particularly flood based disasters are more challenging. Every year floods occur in many regions of the world and cause great losses. In order to monitor and assess such situations, decision-makers need accurate near real-time knowledge of the field situation. How to provide actual information to decision-makers for effective flood monitoring and mitigation is an important task, from the point of view of public welfare. Over-estimation of the flooded area leads to over-compensation to people, while under-estimation results in production loss and negative impacts on the population. Hence it is essential to assess the flood damage accurately, both in qualitative and quantitative terms. In such situations, land cover maps play a very critical role. Updating land cover maps is a time consuming and costlier operation when it is performed using traditional or manual methods. Hence, there is a need to find solutions for such problem through automation. Design of automatic systems dedicated to satellite image processing which involves change detection to discriminate areas of land cover change between imaging dates. The system integrates the spectral and spatial information with the techniques of image registration and pattern classification using nature inspired techniques. In the literature, various works have been carried out for solving the problem of image registration and pattern classification using conventional methods. Many researchers have proved, for different situations, that nature inspired techniques are promising in comparison with that of conventional methods. The main advantage of nature inspired technique over any other conventional methods is its stochastic nature, which converges to optimal solution for any dynamic variation in a given satellite image. Results are given in such terms as to delineate change in multi-date imagery using change-versus-no-change information to guide multi-date data analysis. The main objective of this study is to analyze spatio-temporal satellite data to bring out significant changes in the land cover map through automated image processing methods. In this study, for satellite image analysis of flood assessment and land cover mapping, the study areas and images considered are: Multi-temporal MODerate-resolution Imaging Spectroradiometer (MODIS) image around Krishna river basin in Andhra Pradesh India; Linear Imaging Self Scanning Sensor III (LISS III)and Synthetic Aperture Radar(SAR)image around Kosi river basin in Bihar, India; Landsat7thematicmapperimage from the southern part of India; Quick-Bird image of the central Bangalore, India; Hyperion image around Meerut city, Uttar Pradesh, India; and Indian pines hyperspectral image. In order to develop a flood assessment framework for this study, a database was created from remotely sensed images (optical and/or Synthetic Aperture Radar data), covering a period of time. The nature inspired techniques are used to find solutions to problems of image registration and pattern classification of a multi-sensor and multi-temporal satellite image. Results obtained are used to localize and estimate accurately the flood extent and also to identify the type of the inundated area based on land cover mapping. The nature inspired techniques used for satellite image processing are Artificial Neural Network (ANN), Genetic Algorithm (GA),Particle Swarm Optimization (PSO), Firefly Algorithm(FA),Glowworm Swarm Optimization(GSO)and Artificial Immune System (AIS). From the obtained results, we evaluate the performance of the methods used for image registration and pattern classification to compare the accuracy of satellite image processing using nature inspired techniques. In summary, the main contributions of this thesis include (a) analysis of flood assessment and land cover mapping using satellite images and (b) efficient image registration and pattern classification using nature inspired algorithms, which are more popular than conventional optimization methods because of their simplicity, parallelism and convergence of the population towards the optimal solution in a given search space.