Earth imaging with microsatellites: An investigation, design, implementation and in-orbit demonstration of electronic imaging systems for earth observation on-board low-cost microsatellites.

This research programme has studied the possibilities and difficulties of using 50 kg microsatellites to perform remote imaging of the Earth. The design constraints of these missions are quite different to those encountered in larger, conventional spacecraft. While the main attractions of microsatellites are low cost and fast response times, they present the following key limitations: Payload mass under 5 kg, Continuous payload power under 5 Watts, peak power up to 15 Watts, Narrow communications bandwidths (9.6 / 38.4 kbps), Attitude control to within 5°, No moving mechanics. The most significant factor is the limited attitude stability. Without sub-degree attitude control, conventional scanning imaging systems cannot preserve scene geometry, and are therefore poorly suited to current microsatellite capabilities. The foremost conclusion of this thesis is that electronic cameras, which capture entire scenes in a single operation, must be used to overcome the effects of the satellite's motion. The potential applications of electronic cameras, including microsatellite remote sensing, have erupted with the recent availability of high sensitivity field-array CCD (charge-coupled device) image sensors. The research programme has established suitable techniques and architectures necessary for CCD sensors, cameras and entire imaging systems to fulfil scientific/commercial remote sensing despite the difficult conditions on microsatellites. The author has refined these theories by designing, building and exploiting in-orbit five generations of electronic cameras. The major objective of meteorological scale imaging was conclusively demonstrated by the Earth imaging camera flown on the UoSAT-5 spacecraft in 1991. Improved cameras have since been carried by the KITSAT-1 (1992) and PoSAT-1 (1993) microsatellites. PoSAT-1 also flies a medium resolution camera (200 metres) which (despite complete success) has highlighted certain limitations of microsatellites for high resolution remote sensing. A reworked, and extensively modularised, design has been developed for the four camera systems deployed on the FASat-Alfa mission (1995). Based on the success of these missions, this thesis presents many recommendations for the design of microsatellite imaging systems. The novelty of this research programme has been the principle of designing practical camera systems to fit on an existing, highly restrictive, satellite platform, rather than conceiving a fictitious small satellite to support a high performance scanning imager. This pragmatic approach has resulted in the first incontestable demonstrations of the feasibility of remote sensing of the Earth from inexpensive microsatellites

Machine vision techniques for inspection of dry-fibre composite preforms in the aerospace industry

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents the results of a three year investigation into machine vision techniques for in-process automated inspection of dry-fibre composite preforms. Efficient texture analysis based techniques have been developed, tested, and implemented in a prototype robotic assembly cell. Industrial constraints have been considered in the development of all the algorithms described. A single channel texture analysis model is described which can successfully segment images containing only a few textures. The model is based on convolution of the image with small kernels optimised for the task, and is elegant in the sense that it is computationally simple and easily realisable in low cost hardware. A new convolution kernel optimisation algorithm is described. It is demonstrated that convolution kernels can also be optimised to perform as edge operators in simple textured images. A novel boundary refinement algorithm is described which reduces the inspection errors inherent in texture based boundary estimates. The algorithm takes the form of a local search, using the texture estimate as a guiding template, and selects edge points by maximising a merit function. Optimum parameters for the merit function are obtained using multiple training images in conjunction with simple function optimisation algorithms.This study is funded by the Engineering and Physical Sciences Research Council (EPSRC) and Dowty Aerospace Propellers Ltd

Video based vehicle detection for advance warning Intelligent Transportation System

Video based vehicle detection and surveillance technologies are an integral part of Intelligent Transportation System (ITS), due to its non-intrusiveness and capability or capturing global and specific vehicle behavior data. The initial goal of this thesis is to develop an efficient advance warning ITS system for detection of congestion at work zones and special events based on video detection. The goals accomplished by this thesis are: (1) successfully developed the advance warning ITS system using off-the-shelf components and, (2) Develop and evaluate an improved vehicle detection and tracking algorithm. The advance warning ITS system developed includes many off-the-shelf equipments like Autoscope (video based vehicle detector), Digital Video Recorders, RF transceivers, high gain Yagi antennas, variable message signs and interface processors. The video based detection system used requires calibration and fine tuning of configuration parameters for accurate results. Therefore, an in-house video based vehicle detection system was developed using the Corner Harris algorithm to eliminate the need of complex calibration and contrasts modifications. The algorithm was implemented using OpenCV library on a Arcom\u27s Olympus Windows XP Embedded development kit running WinXPE operating system. The algorithm performance is for accuracy in vehicle speed and count is evaluated. The performance of the proposed algorithm is equivalent or better to the Autoscope system without any modifications to calibration and lamination adjustments

Image Processing and Pattern Recognition Applied to Soil Structure

This thesis represents a collaborative research between the Department of Electronics & Electrical Engineering and the Department of Civil Engineering, University of Glasgow. The project was initially aimed at development of some theories and techniques of image processing and pattern recognition for the study of soil microstructures. More specifically, the aim was to study the shapes, orientations, and arrangements of soil particles and voids (i.e. pores): these three are very important properties, which are used both for description, recognition and classification of soils, and also for studying the relationships between the soil structures and physical, chemical, geological, geographical, and environmental changes. The work presented here was based principally on a need for analysing the structure of soil as recorded in two-dimensional images which might be conventional photographs, optical micrographs, or electron-micrographs. In this thesis, first a brief review of image processing and pattern recognition and their previous application in the study of soil microstructures is given. Then a convex hull based shape description and classification for soil particles is presented. A new algorithm, SPCH, is proposed for finding the convex hull of either a binary object or a cluster of points in a plane. This algorithm is efficient and reliable. Features of pattern vectors for shape description and classification are obtained from the convex hull and the object. These features are invariant with respect to coordinate rotation, translation, and scaling. The objects can then be classified by any standard feature-space method: here minimum-distance classification was used. Next the orientation analysis of soil particles is described. A new method, Directed Vein, is proposed for the analysis. Another three methods: Convex Hull, Principal Components, and Moments, are also presented. Comparison of the four methods shows that the Directed Vein method appears the fastest; but it also has the special property of estimating an 'internal preferred orientation' whereas the other methods estimate an 'elongation direction'. Fourth, the roundness/sharpness analysis of soil particles is presented. Three new algorithms, referred to as the Centre, Gradient Centre, and Radius methods, all based on the Circular Hough Transform, are proposed. Two traditional Circular Hough Transform algorithms are presented as well. The three new methods were successfully applied to the measurement of the roundness (sharpness of comers) of two-dimensional particles. The five methods were compared from the points of view of memory requirement, speed, and accuracy; and the Radius method appears to be the best for the special topic of sharpness/roundness analysis. Finally the analysis and classification of aggregates of objects is introduced. A new method. Extended Linear Hough Transform, is proposed. In this method, the orientations and locations of the objects are mapped into extended Hough space. The arrangements of the objects within an aggregate are then determined by analysing the data distributions in this space. The aggregates can then be classified using a tree classifier. Taken together, the methods developed or tested here provide a useful toolkit for analysing the shapes, orientation, and aggregation of particles such as those seen in two-dimensional images of soil structure at various scales

Adaptive visual sampling

PhDVarious visual tasks may be analysed in the context of sampling from the visual field. In visual psychophysics, human visual sampling strategies have often been shown at a high-level to be driven by various information and resource related factors such as the limited capacity of the human cognitive system, the quality of information gathered, its relevance in context and the associated efficiency of recovering it. At a lower-level, we interpret many computer vision tasks to be rooted in similar notions of contextually-relevant, dynamic sampling strategies which are geared towards the filtering of pixel samples to perform reliable object association. In the context of object tracking, the reliability of such endeavours is fundamentally rooted in the continuing relevance of object models used for such filtering, a requirement complicated by realworld conditions such as dynamic lighting that inconveniently and frequently cause their rapid obsolescence. In the context of recognition, performance can be hindered by the lack of learned context-dependent strategies that satisfactorily filter out samples that are irrelevant or blunt the potency of models used for discrimination. In this thesis we interpret the problems of visual tracking and recognition in terms of dynamic spatial and featural sampling strategies and, in this vein, present three frameworks that build on previous methods to provide a more flexible and effective approach. Firstly, we propose an adaptive spatial sampling strategy framework to maintain statistical object models for real-time robust tracking under changing lighting conditions. We employ colour features in experiments to demonstrate its effectiveness. The framework consists of five parts: (a) Gaussian mixture models for semi-parametric modelling of the colour distributions of multicolour objects; (b) a constructive algorithm that uses cross-validation for automatically determining the number of components for a Gaussian mixture given a sample set of object colours; (c) a sampling strategy for performing fast tracking using colour models; (d) a Bayesian formulation enabling models of object and the environment to be employed together in filtering samples by discrimination; and (e) a selectively-adaptive mechanism to enable colour models to cope with changing conditions and permit more robust tracking. Secondly, we extend the concept to an adaptive spatial and featural sampling strategy to deal with very difficult conditions such as small target objects in cluttered environments undergoing severe lighting fluctuations and extreme occlusions. This builds on previous work on dynamic feature selection during tracking by reducing redundancy in features selected at each stage as well as more naturally balancing short-term and long-term evidence, the latter to facilitate model rigidity under sharp, temporary changes such as occlusion whilst permitting model flexibility under slower, long-term changes such as varying lighting conditions. This framework consists of two parts: (a) Attribute-based Feature Ranking (AFR) which combines two attribute measures; discriminability and independence to other features; and (b) Multiple Selectively-adaptive Feature Models (MSFM) which involves maintaining a dynamic feature reference of target object appearance. We call this framework Adaptive Multi-feature Association (AMA). Finally, we present an adaptive spatial and featural sampling strategy that extends established Local Binary Pattern (LBP) methods and overcomes many severe limitations of the traditional approach such as limited spatial support, restricted sample sets and ad hoc joint and disjoint statistical distributions that may fail to capture important structure. Our framework enables more compact, descriptive LBP type models to be constructed which may be employed in conjunction with many existing LBP techniques to improve their performance without modification. The framework consists of two parts: (a) a new LBP-type model known as Multiscale Selected Local Binary Features (MSLBF); and (b) a novel binary feature selection algorithm called Binary Histogram Intersection Minimisation (BHIM) which is shown to be more powerful than established methods used for binary feature selection such as Conditional Mutual Information Maximisation (CMIM) and AdaBoost

JPEG-like Image Compression using Neural-network-based Block Classification and Adaptive Reordering of Transform Coefficients

The research described in this thesis addresses aspects of coding of discrete-cosinetransform (DCT) coefficients, that are present in a variety of transform-based digital-image-compression schemes such as JPEG. Coefficient reordering; that directly affects the symbol statistics for entropy coding, and therefore the effectiveness of entropy coding; is investigated. Adaptive zigzag reordering, a novel versatile technique that achieves efficient reordering by processing variable-size rectangular sub-blocks of coefficients, is developed. Classification of blocks of DCT coefficients using an artificial neural network (ANN) prior to adaptive zigzag reordering is also considered. Some established digital-image-compression techniques are reviewed, and the JPEG standard for the DCT-based method is studied in more detail. An introduction to artificial neural networks is provided. Lossless conversion of blocks of coefficients using adaptive zigzag reordering is investigated, and experimental results are presented. A versatile algorithm, that generates zigzag scan paths for sub-blocks of any dimensions using a binary decision tree, is developed. An implementation of the algorithm based on programmable logic devices (PLDs) is described demonstrating the feasibility of hardware implementations. Coding of the sub-block dimensions, that need to be retained in order to reconstruct a sub-block during decoding, based on the scan-path length is developed. Lossy conversion of blocks of coefficients is also considered, and experimental results are presented. A two-layer feedforward artificial neural network trained using an error-backpropagation algorithm, that determines the sub-block dimensions, is described. Isolated nonzero coefficients of small significance are discarded in some blocks, and therefore smaller sub-blocks are generated

The 1991 research and technology report, Goddard Space Flight Center

The 1991 Research and Technology Report for Goddard Space Flight Center is presented. Research covered areas such as (1) earth sciences including upper atmosphere, lower atmosphere, oceans, hydrology, and global studies; (2) space sciences including solar studies, planetary studies, Astro-1, gamma ray investigations, and astrophysics; (3) flight projects; (4) engineering including robotics, mechanical engineering, electronics, imaging and optics, thermal and cryogenic studies, and balloons; and (5) ground systems, networks, and communications including data and networks, TDRSS, mission planning and scheduling, and software development and test

First Annual Workshop on Space Operations Automation and Robotics (SOAR 87)

Several topics relative to automation and robotics technology are discussed. Automation of checkout, ground support, and logistics; automated software development; man-machine interfaces; neural networks; systems engineering and distributed/parallel processing architectures; and artificial intelligence/expert systems are among the topics covered