While most recent work on synthetic aperture radar (SAR) image segmentation has concentrated on accurately segmenting single channel images, many SAR applications involve multi-channel analysis. Hence, if segmentation...
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While most recent work on synthetic aperture radar (SAR) image segmentation has concentrated on accurately segmenting single channel images, many SAR applications involve multi-channel analysis. Hence, if segmentation algorithms are going to be of real use, they must be applicable to multi-channel images. The aim multi-channel segmentation is to produce a single region labeling from the multi-channel input. Each segment should represent a homogeneous region that differs statistically from each adjacent segment. Segment boundary maps may be used to investigate structural change in multi-temporal imagery. Alternatively, the multi-channel image may be classified based on parameters estimated within segments, guaranteeing that parameters are estimated over the largest available homogeneous regions.
Low Earth orbit provides an ideal operating environment for synthetic aperture radar (SAR) imaging due to well-defined platform motion. Airborne SAR platforms do not benefit from this luxury and suffer unpredictable m...
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Low Earth orbit provides an ideal operating environment for synthetic aperture radar (SAR) imaging due to well-defined platform motion. Airborne SAR platforms do not benefit from this luxury and suffer unpredictable motions causing image defocusing. Autofocus is a widely acknowledged technique for correcting these motions, causing the image to be properly focused. Accurate position and velocity information is required to process spaceborne SAR scenes with higher spatial resolutions requiring greater knowledge of the satellite's orbit. The availability of position and velocity data is dependant on the efficiency and location of the ground station and could finally lead to delays in processing times. In these cases an orbital propagation model has to be employed for immediate processing. The precision of the image is now dependent on the accuracy of the orbit model used. Using DRA patented autofocus/phase correction techniques on ERS1 raw data it is shown that the point target response of imagery obtained with no prior orbit knowledge is comparable with precision imagery. In addition the technique allows continuous strip map imagery to be produced with no discontinuities.
The proceedings contains 7 papers. Some of the specific topics discussed are: detection of unusual events in remote sensed imagery;detection of typical image features;subsea image interpretation - a problem in remote ...
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The proceedings contains 7 papers. Some of the specific topics discussed are: detection of unusual events in remote sensed imagery;detection of typical image features;subsea image interpretation - a problem in remotesensing;and a design procedure for the design of advanced coded pulse radar waveforms generated from a finite set of samples.
Polarimetric images provide the means of observing objects and phenomena that are invisible to conventional imaging systems. An imaging polarimeter produces images representing the spatial variation in polarisation ov...
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Polarimetric images provide the means of observing objects and phenomena that are invisible to conventional imaging systems. An imaging polarimeter produces images representing the spatial variation in polarisation over the scene, which can be related to true texture, surface structure and material type. To date, polarimetric imagery has been completely neglected as a powerful, non-intrusive intrusive visual probing tool. Therefore, to provide an adequate perspective on the remotesensing capability of an imaging polarimeter, and how polarimetry could become an important technique for visual proving, the relevant theoretical basis for the processing of polarimetric images is presented.
A set of adaptive transforms is defined which emphasises atypical features in an image. The transforms emphasise features such as regions of atypical grey level, boundaries, or unusual multispectral properties. The re...
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A set of adaptive transforms is defined which emphasises atypical features in an image. The transforms emphasise features such as regions of atypical grey level, boundaries, or unusual multispectral properties. The result of the transforms is a new image in which the difference between an atypical feature and the rest of the image has been enhanced.
This paper illustrates statistical techniques which can be applied to acoustic imagery to compensate for the lack of multispectral information. Fractal segmentation appears to be a very powerful technique for discrimi...
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This paper illustrates statistical techniques which can be applied to acoustic imagery to compensate for the lack of multispectral information. Fractal segmentation appears to be a very powerful technique for discriminating local variations in the sea-floor, which is important in enabling boundaries between different regions to be delineated. The measurement of spectral attenuation in the sub-bottom provides a possible tool for obtaining information on geotechnical properties of the sediments. This type of information could reduce the requirement for seabed coring surveys which are very expensive and time consuming.
This paper describes a novel technique for the detection of unusual events in remote sensed imagery when the images contain a high level of background clutter. The technique combines conventional imageprocessing algo...
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This paper describes a novel technique for the detection of unusual events in remote sensed imagery when the images contain a high level of background clutter. The technique combines conventional imageprocessing algorithms with statistical methods to derive simple models of image backgrounds. These fractal-based models have been shown experimentally to give a good fit to statistics derived from remote sensed imagery of structured natural backgrounds such as mountain ranges and the sea surface. Unusual events, such as man-made objects show statistics that differ markedly from the background models and can be detected by finding differences between measured image statistics and the assumed background model.
The MuSIP project, part funded by the European ESPRIT programme for research in information, aims to develop a software demonstrator for the automatic fusion and analysis of multisource data. The project recognises th...
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The MuSIP project, part funded by the European ESPRIT programme for research in information, aims to develop a software demonstrator for the automatic fusion and analysis of multisource data. The project recognises this need across all computer vision applications and so will test the demonstrator on two dissimilar applications, one in the field of remotesensing and one in the field of medical image analysis. The target remotesensing application is the monitoring of forestry using a dataset of radar and optical imagery, map and ground data. This paper introduces the modules that comprise the demonstrator that is: a supervisor with its associated knowledge bases, algorithms and the databases. Attention is focussed in particular on the role of knowledge bases and the methods of data fusion.
A set of adaptive transforms is defined which emphasises atypical features in an image. The transforms emphasise features such as regions of atypical grey level, boundaries, or unusual multispectral properties. The re...
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A set of adaptive transforms is defined which emphasises atypical features in an image. The transforms emphasise features such as regions of atypical grey level, boundaries, or unusual multispectral properties. The result of the transforms is a new image in which the difference between an atypical feature and the rest of the image has been enhanced.< >
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