Increasingly demanding military requirements and rapid technological advances are producing reconnaissance sensors with greater spatial, spectral and temporal resolution. This, with the benefits to be gained from deploying multiple sensors co-operatively, is resulting in a so-called data deluge, where recording systems, data-links, and exploitation systems struggle to cope with the required imagery throughput. This paper focuses on the exploitation stage and, in particular, the provision of cueing aids for Imagery Analysts (IAs), who need to integrate a variety of sources in order to gain situational awareness. These sources may include multi-source imagery and intelligence feeds, various types of mapping and collateral data, as well the need for the IAs to add their own expertise in military doctrine etc. This integration task is becoming increasingly difficult as the volume and diversity of the input increases. The first stage in many exploitation tasks is that of image registration. It facilitates change detection and many avenues of multi-source exploitation. Progress is reported on the automating this task, on its current performance characteristics, its integration into a potentially operational system, and hence on its expected utility. We also report on the development of an evolutionary architecture, 'ICARUS' in which feature detectors (or cuers) are constructed incrementally using a genetic algorithm that evolves simple sub-structures before combining, and further evolving them, to form more comprehensive and robust detectors. This approach is shown to help overcome the complexity limit that prevents many machine-learning algorithms from scaling up to the real world.
The introduction of soft copy exploitation systems for ISR in the UK has resulted in a degree of stovepiping and inflexibility. The UK are developing the JSIES concept for future, which provides a baseline for the range of exploitation systems to aim for. This marker is in terms of a set of common and evolving building blocks in architecture, interfaces that cater for all three services applications, extended support for the Image Analyst, and a variety of modular and scaleable configurations. This paper describes the military drivers and issues behind the JSIES concept, before outlining the main threads of the project with a particular emphasis on the assisted target detection and recognition aspects.
A new type of deformable model is presented that is able to combine some of the characteristics of both snakes and templates. It can be used to segment and recognize two dimensional objects when only vague prior knowledge about their shapes is available. A jump-diffusion process is used to fit the template to the image. The jumps allows the template to undergo abrupt discontinuous changes in shape and position and to decide among multiple target models. The diffusion process allows the template to perform continuous flowing deformations like a snake. A prior shape model is described that uses the local and global characteristics of each different target class. An efficient form for the image likelihood is given that extends to multiple attributes and multiple images. The jump transition kernel defines the probabilities of the template jumping to a new state. This is difficult to generate and sample in practice though. To allow for this a method is described where a marginal transition kernel is generated by integrating over the continuous internal parameters for subsets of jumps. This makes the sampling problem much easier while still providing effective inferencing. The relation of this approach to active contours and region competition is discussed. It is shown that with the appropriate choice of prior and likelihood that snakes can easily be modelled within the deterministic part of the diffusion process. The method is demonstrated with the detection of buildings and planes in infrared and optical images and a comparison with an active contour is also given.
One of the difficulties that has been apparent in applying image processing algorithms not just for automatic target recognition but also for associated tasks in image processing and understanding is that of the optimal choice of parameters and algorithms. Firstly we must select an algorithm to use and secondly the actual parameters that are required by that algorithm. It is also the case that using a chosen algorithm on a different image class yields results of a totally different quality, here we consider three image classes, namely infra-red linescan, dd5-Russian satellite and SPOT imagery. We are now exploring the use of genetic algorithms for the purpose of parameter and algorithm selection and will show how the approach can successfully obtain results which in the past have tended to be obtained somewhat heuristically.
Object segmentation is the process by which a mask is generated which identifies the area of an image which is occupied by an object. Many object recognition techniques depend on the quality of such masks for shape and underlying brightness information, however, segmentation remains notoriously unreliable. This paper considers how the image restoration technique of Geman and Geman can be applied to the improvement of object segmentations generated by a locally adaptive background subtraction technique. Also presented is how an artificial neural network hybrid, consisting of a single layer Kohonen network with each of its nodes connected to a different multi-layer perceptron, can be used to approximate the image restoration process. It is shown that the restoration techniques are very well suited for parallel processing and in particular the artificial neural network hybrid has the potential for near real time image processing. Results are presented for the detection of ships in SPOT panchromatic imagery and the detection of vehicles in infrared linescan images, these being a fair representation of the wider class of problem.
Progress is reviewed on the development of an all source image interpretation system which exploits complementary evidence from a range of experts. This co-operation may occur between feature detectors in different bands, between detectors searching for different types of feature, or between different types of detector of the same feature. Algorithms for detecting vehicles in infrared linescan imagery gives a low missed detection rate but have been found to respond falsely to: roads fragmented by trees; structures such as cylindrical storage tanks; and to corners of man made objects, such as buildings. False alarms are reduced by applying algorithms which detect subclasses of false alarms reliably i.e. buildings and storage tanks. In addition, both are features of interest in themselves, and are useful primitives in the identification of sites. The integration of depth (in the form of disparity maps) is examined as a means of reducing false building detections. Outputs from the feature detectors are combined using a simple rule-based approach. A surface based model matching technique is examined as a means of classifying the remaining vehicle candidates.
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