6 research outputs found
Multistage classification of multispectral Earth observational data: The design approach
An algorithm is proposed which predicts the optimal features at every node in a binary tree procedure. The algorithm estimates the probability of error by approximating the area under the likelihood ratio function for two classes and taking into account the number of training samples used in estimating each of these two classes. Some results on feature selection techniques, particularly in the presence of a very limited set of training samples, are presented. Results comparing probabilities of error predicted by the proposed algorithm as a function of dimensionality as compared to experimental observations are shown for aircraft and LANDSAT data. Results are obtained for both real and simulated data. Finally, two binary tree examples which use the algorithm are presented to illustrate the usefulness of the procedure
Scale-invariant segmentation of dynamic contrast-enhanced perfusion MR-images with inherent scale selection
Selection of the best set of scales is problematic when developing signaldriven
approaches for pixel-based image segmentation. Often, different
possibly conflicting criteria need to be fulfilled in order to obtain the best tradeoff
between uncertainty (variance) and location accuracy. The optimal set of
scales depends on several factors: the noise level present in the image material,
the prior distribution of the different types of segments, the class-conditional
distributions associated with each type of segment as well as the actual size of
the (connected) segments. We analyse, theoretically and through experiments,
the possibility of using the overall and class-conditional error rates as criteria
for selecting the optimal sampling of the linear and morphological scale spaces.
It is shown that the overall error rate is optimised by taking the prior class
distribution in the image material into account. However, a uniform (ignorant)
prior distribution ensures constant class-conditional error rates. Consequently,
we advocate for a uniform prior class distribution when an uncommitted, scaleinvariant
segmentation approach is desired.
Experiments with a neural net classifier developed for segmentation of
dynamic MR images, acquired with a paramagnetic tracer, support the
theoretical results. Furthermore, the experiments show that the addition of
spatial features to the classifier, extracted from the linear or morphological
scale spaces, improves the segmentation result compared to a signal-driven
approach based solely on the dynamic MR signal. The segmentation results
obtained from the two types of features are compared using two novel quality
measures that characterise spatial properties of labelled images
Modeling, Simulation, and Analysis of Optical Remote Sensing Systems
Remote Sensing of the Earth\u27s resources from space-based sensors has evolved in the past twenty years from a scientific experiment to a commonly used technological tool. The scientific applications and engineering aspects of remote sensing systems have been studied extensively. However, most of these studies have been aimed at understanding individual aspects of the remote sensing process while relatively few have studied their interrelations. A motivation for studying these interrelationships has arisen with the advent of highly sophisticated configurable sensors as part of the Earth Observing System (EOS) proposed by NASA for the 1990\u27s. These instruments represent a tremendous advance in sensor technology with data gathered In nearly 200 spectral bands, and with the ability for scientists to specify many observational parameters. It will be increasingly necessary for users of remote sensing systems to understand the tradeoffs and interrelationships of system parameters. In this report, two approaches to investigating remote sensing systems are developed. In one approach, detailed models of the scene, the sensor, and the processing aspects of the system are implemented In a discrete simulation, This approach is useful in creating simulated images with desired characteristics for use in sensor or processing algorithm development. A less complete, but computationally simpler method based on a parametric model of the system is also developed. In this analytical model the various informational classes are parameterized by their spectral mean vector and covariance matrix. These Class statistics are modified by models for the atmosphere, the sensor, and processing algorithms and an estimate made of the resulting classification accuracy among the informational classes. Application of these models is made to the study of the proposed High Resolution Imaging Spectrometer (HIRIS).; The interrelationships among observational conditions, sensor effects, and processing choices are investigated with several interesting results. Reduced classification accuracy in hazy atmospheres is seen to be due not only to sensor noise, but also to the increased path radiance scattered from the surface. The effect of the atmosphere is also seen in its relationship to view angle. In clear atmospheres, increasing the zenith view angle is seen to result in an increase in classification accuracy due to the reduced scene variation as the ground size of image pixels is increased. However, in hazy atmospheres the reduced transmittance and increased path radiance counter this effect and result in decreased accuracy with increasing view angle. The relationship between the Signal-to:Noise Ratio (SNR) and classification accuracy is seen to depend in a complex manner on spatial parameters and feature selection. Higher SNR values are seen to hot always result in higher accuracies, and even in cases of low SNR feature sets chosen appropriately can lead to high accuracies
The analytical design of spectral measurements for multispectral remote sensor systems
The author has identified the following significant results. In order to choose a design which will be optimal for the largest class of remote sensing problems, a method was developed which attempted to represent the spectral response function from a scene as accurately as possible. The performance of the overall recognition system was studied relative to the accuracy of the spectral representation. The spectral representation was only one of a set of five interrelated parameter categories which also included the spatial representation parameter, the signal to noise ratio, ancillary data, and information classes. The spectral response functions observed from a stratum were modeled as a stochastic process with a Gaussian probability measure. The criterion for spectral representation was defined by the minimum expected mean-square error
Pattern recognition with discrete and mixed data : theory and practice
This thesis is devoted to aspects related to the analysis of medical data bases in the
context of pattern recognition. It contains both theoretical aspects and practical
applications and its scope includes questions and problems that arise when
applying pattern recognition methods and techniques to this type of data. The goal of the application of statistical pattern recognition techniques to medical
records, is the classification of the (disease) patterns that may be present in such
records in terms of the information they contain. Typically, a medical record
contains a description of history, symptoms, results from laboratory tests, signals,
etc., all related to a given patient, i.e. all the information normally required by a
physician when making a diagnosis and/or a prognosis. Pattern recognition may
be used in order to obtain procedures (computer implemented algorithms) to assign
diagnostic or prognostic classes to a given patient, on the basis of information also
used by a physician. These procedures are not intended to replace but to assist the
physician in the decision making process. The procedures are called classifiers or
discriminants and the symptoms, signals, etc., are called features. Each individual
record is termed an object, and a collection of objects with qualitatively and/or
quantitatively similar characteristics, as established by an expert, is called a class.
It should be clear that pattern recognition can be applied to a wide variety of areas
and problems, of which (computer-aided) medical decision making is just an
example.
In order to arrive at a classifier and restricting ourselves to what is called
supervised learning, a set of objects known a-priori to belong to two or more
classes (depending on the problem at hand) is needed. In this set, each object must
be represented by a group of features and have a class assigned to it. The role of
medical data bases is now clear: they are the set of objects required for supervised
learning