Intensity based image registration of satellite images using evolutionary techniques

Image registration is the fundamental image processing technique to determine geometrical transformation that gives the most accurate match between reference and floating images. Its main aim is to align two images. Satellite images to be fused for numerous applications must be registered before use. The main challenges in satellite image registration are finding out the optimum transformation parameters. Here in this work the non-alignment parameters are considered to be rigid and affine transformation. An intensity based satellite image registration technique is being used to register the floating image to the native co-ordinate system where the normalized mutual information (NMI) is taken as the similarity metric for optimizing and updating transform parameters. Because of no assumptions are made regarding the nature of the relationship between the image intensities in both modalities NMI is very general and powerful and can be applied automatically without prior segmentation on a large variety of data and as well works better for overlapped images as compared to mutual information(MI). In order to get maximum accuracy of registration the NMI is optimized using Genetic algorithm, particle swarm optimization and hybrid GA-PSO. The random initialization and computational complexity makes GA oppressive, whereas weak local search ability with a premature convergence is the main drawback of PSO. Hybrid GA-PSO makes a trade-off between the local and global search in order to achieve a better balance between convergence speed and computational complexity. The above registration algorithm is being validated with several satellite data sets. The hybrid GA-PSO outperforms in terms of optimized NMI value and percentage of mis-registration error

Multi-Sensor Image Registration, Fusion and Dimension Reduction

With the development of future spacecraft formations comes a number of complex challenges such as maintaining precise relative position and specified attitudes, as well as being able to communicate with each other. More generally, with the advent of spacecraft formations, issues related to performing on-board and automatic data computing and analysis as well as decision planning and scheduling will figure among the most important requirements. Among those, automatic image registration, image fusion and dimension reduction represent intelligent technologies that would reduce mission costs,would enable autonomous decisions to be taken on-board, and would make formation flying adaptive, self-reliant, and cooperative. For both on-board and on-the-ground applications, the particular need for dimension reduction is two-fold, first to reduce the communication bandwidth, second as a pre-processing to make computations feasible,simpler and faster

HSI-MSER: Hyperspectral Image Registration Algorithm based on MSER and SIFT

Image alignment is an essential task in many applications of hyperspectral remote sensing images. Before any processing, the images must be registered. The Maximally Stable Extremal Regions (MSER) is a feature detection algorithm which extracts regions by thresholding the image at different grey levels. These extremal regions are invariant to image transformations making them ideal for registration. The Scale-Invariant Feature Transform (SIFT) is a well-known keypoint detector and descriptor based on the construction of a Gaussian scale-space. This article presents a hyperspectral remote sensing image registration method based on MSER for feature detection and SIFT for feature description. It efficiently exploits the information contained in the different spectral bands to improve the image alignment. The experimental results over nine hyperspectral images show that the proposed method achieves a higher number of correct registration cases using less computational resources than other hyperspectral registration methods. Results are evaluated in terms of accuracy of the registration and also in terms of execution timeMinisterio de Ciencia e Innovación, Government of Spain PID2019-104834GB-I00; Consellería de Cultura, Educación e Universidade (Grant Number: ED431C 2018/19 and 2019-2022 ED431G-2019/04); Junta de Castilla y León under Project VA226P20; 10.13039/501100008530-European Regional Development Fund; Ministerio de Universidades, Government of Spain (Grant Number: FPU16/03537)S

Automatic registration of multi-modal airborne imagery

This dissertation presents a novel technique based on Maximization of Mutual Information (MMI) and multi-resolution to design an algorithm for automatic registration of multi-sensor images captured by various airborne cameras. In contrast to conventional methods that extract and employ feature points, MMI-based algorithms utilize the mutual information found between two given images to compute the registration parameters. These, in turn, are then utilized to perform multi-sensor registration for remote sensing images. The results indicate that the proposed algorithms are very effective in registering infrared images taken at three different wavelengths with a high resolution visual image of a given scene. The MMI technique has proven to be very robust with images acquired with the Wild Airborne Sensor Program (WASP) multi-sensor instrument. This dissertation also shows how wavelet based techniques can be used in a multi-resolution analysis framework to significantly increase computational efficiency for images captured at different resolutions. The fundamental result of this thesis is the technique of using features in the images to enhance the robustness, accuracy and speed of MMI registration. This is done by using features to focus MMI on places that are rich in information. The new algorithm smoothly integrates with MMI and avoids any need for feature-matching, and then the applications of such extensions are studied. The first extension is the registration of cartographic maps and image datum, which is very important for map updating and change detection. This is a difficult problem because map features such as roads and buildings may be mis-located and features extracted from images may not correspond to map features. Nonetheless, it is possible to obtain a general global registration of maps and images by applying statistical techniques to map and image features. To solve the map-to-image registration problem this research extends the MMI technique through a focus-of-attention mechanism that forces MMI to utilize correspondences that have a high probability of being information rich. The gradient-based parameter search and exhaustive parameter search methods are also compared. Both qualitative and quantitative analysis are used to assess the registration accuracy. Another difficult application is the fusion of the LIDAR elevation or intensity data with imagery. Such applications are even more challenging when automated registrations algorithms are needed. To improve the registration robustness, a salient area extraction algorithm is developed to overcome the distortion in the airborne and satellite images from different sensors. This extension combines the SIFT and Harris feature detection algorithms with MMI and the Harris corner label map to address difficult multi-modal registration problems through a combination of selection and focus-of-attention mechanisms together with mutual information. This two-step approach overcomes the above problems and provides a good initialization for the final step of the registration process. Experimental results are provided that demonstrate a variety of mapping applications including multi-modal IR imagery, map and image registration and image and LIDAR registration

Pixel-level Image Fusion Algorithms for Multi-camera Imaging System

This thesis work is motivated by the potential and promise of image fusion technologies in the multi sensor image fusion system and applications. With specific focus on pixel level image fusion, the process after the image registration is processed, we develop graphic user interface for multi-sensor image fusion software using Microsoft visual studio and Microsoft Foundation Class library. In this thesis, we proposed and presented some image fusion algorithms with low computational cost, based upon spatial mixture analysis. The segment weighted average image fusion combines several low spatial resolution data source from different sensors to create high resolution and large size of fused image. This research includes developing a segment-based step, based upon stepwise divide and combine process. In the second stage of the process, the linear interpolation optimization is used to sharpen the image resolution. Implementation of these image fusion algorithms are completed based on the graphic user interface we developed. Multiple sensor image fusion is easily accommodated by the algorithm, and the results are demonstrated at multiple scales. By using quantitative estimation such as mutual information, we obtain the experiment quantifiable results. We also use the image morphing technique to generate fused image sequence, to simulate the results of image fusion. While deploying our pixel level image fusion algorithm approaches, we observe several challenges from the popular image fusion methods. While high computational cost and complex processing steps of image fusion algorithms provide accurate fused results, they also makes it hard to become deployed in system and applications that require real-time feedback, high flexibility and low computation abilit