Hyperspectral data analysis procedures with reduced sensitivity to noise

Multispectral sensor systems have become steadily improved over the years in their ability to deliver increased spectral detail. With the advent of hyperspectral sensors, including imaging spectrometers, this technology is in the process of taking a large leap forward, thus providing the possibility of enabling delivery of much more detailed information. However, this direction of development has drawn even more attention to the matter of noise and other deleterious effects in the data, because reducing the fundamental limitations of spectral detail on information collection raises the limitations presented by noise to even greater importance. Much current effort in remote sensing research is thus being devoted to adjusting the data to mitigate the effects of noise and other deleterious effects. A parallel approach to the problem is to look for analysis approaches and procedures which have reduced sensitivity to such effects. We discuss some of the fundamental principles which define analysis algorithm characteristics providing such reduced sensitivity. One such analysis procedure including an example analysis of a data set is described, illustrating this effect

RSSIM: A Simulation Program for Optical Remote Sensing Systems

RSSIM is a comprehensive simulation tool for the study of multispectral remotely sensed images and associated system parameters. It has been developed to allow the creation of realistic multispectral images based on detailed models of the surface the atmosphere, and the sensor. It also can be used to study the effect of system parameters on an output measure, such as classification accuracy or class separability. In this report the operation and use of RSSIM is described. In this first section the implementation of the program is discussed, followed by examples of its use. In section 2 the structure and algorithms used in the major subroutines, along with the associated parameter files are discussed. Section 3 provides a complete listing of the program code

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

Classification of high dimensional multispectral image data

A method for classifying high dimensional remote sensing data is described. The technique uses a radiometric adjustment to allow a human operator to identify and label training pixels by visually comparing the remotely sensed spectra to laboratory reflectance spectra. Training pixels for material without obvious spectral features are identified by traditional means. Features which are effective for discriminating between the classes are then derived from the original radiance data and used to classify the scene. This technique is applied to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data taken over Cuprite, Nevada in 1992, and the results are compared to an existing geologic map. This technique performed well even with noisy data and the fact that some of the materials in the scene lack absorption features. No adjustment for the atmosphere or other scene variables was made to the data classified. While the experimental results compare favorably with an existing geologic map, the primary purpose of this research was to demonstrate the classification method, as compared to the geology of the Cuprite scene

Effect of radiance-to-reflectance transformation and atmosphere removal on maximum likelihood classification accuracy of high-dimensional remote sensing data

Many analysis algorithms for high-dimensional remote sensing data require that the remotely sensed radiance spectra be transformed to approximate reflectance to allow comparison with a library of laboratory reflectance spectra. In maximum likelihood classification, however, the remotely sensed spectra are compared to training samples, thus a transformation to reflectance may or may not be helpful. The effect of several radiance-to-reflectance transformations on maximum likelihood classification accuracy is investigated in this paper. We show that the empirical line approach, LOWTRAN7, flat-field correction, single spectrum method, and internal average reflectance are all non-singular affine transformations, and that non-singular affine transformations have no effect on discriminant analysis feature extraction and maximum likelihood classification accuracy. (An affine transformation is a linear transformation with an optional offset.) Since the Atmosphere Removal Program (ATREM) and the log residue method are not affine transformations, experiments with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were conducted to determine the effect of these transformations on maximum likelihood classification accuracy. The average classification accuracy of the data transformed by ATREM and the log residue method was slightly less than the accuracy of the original radiance data. Since the radiance-to-reflectance transformations allow direct comparison of remotely sensed spectra with laboratory reflectance spectra, they can be quite useful in labeling the training samples required by maximum likelihood classification, but these transformations have only a slight effect or no effect at all on discriminant analysis and maximum likelihood classification accuracy

Use of Unlabeled Samples for Mitigating the Hughes Phenomenon

The use of unlabeled samples in improving the performance of classifiers is studied. When the number of training samples is fixed and small, additional feature measurements may reduce the performance of a statistical classifier. It is shown that by using unlabeled samples, estimates of the parameters can be improved and therefore this phenomenon may be mitigated. Various methods for using unlabeled samples are reviewed and experimental results are provided

Spectral Feature Design In High Dimensional Multispectral Data

The High resolution imaging Spectrometer (HIRIS) is designed to acquire images simultaneously in 192 spectral bands in the 0.4-2,5 μm wavelength region. It will make possible the collection of essentially continuous reflectance spectra at a spectral resolution sufficient to extract significantly enhanced amounts of information from return signals as compared to existing systems. By effectively utilizing these signals, direct identification of the parameters of species can be achieved and their subtle changes can also be observed and measured. The advantages of such high dimensional data come at a cost of increased system and data complexity. For example, since the finer the spectral resolution, the higher the data rate, it becomes impractical to design the sensor to be operated continuously. Even operating HIRIS in a request only mode, its 512 Mbps raw data rate still constitutes a serious communication challenge. In order to solve this problem, it is essential to find new ways to preprocess the data which reduce the data rate while at the same time maintaining the information content of the high dimensional signal produced. In this thesis, four spectral feature design techniques are developed from the Weighted Karhunen-Loeve Transforms, They are : non-overlapping band feature selection algorithm, overlapping band feature selection algorithm, Walsh function approach, and infinite clipped optimal function approach. From a simplicity and effectiveness point of view, the infinite clipped optimal function approach is chosen since the features are easiest to find and their classification performance is the best. This technique approximates the spectra) structure of the optimal features via infinite clipping and results in transform coefficients which are either +1, - 1 or 0. Therefore the necessary processing can be easily implemented on-board the spacecraft by using a set of programmable adders that operate on the grouping instructions received from the ground station. After the preprocessed data has been received at the ground station, canonical analysis is further used to find the best set of features under the criterion that maximal class separability is achieved. In this research, both 100 dimensional vegetation data and 200 dimensional soil data are used to test the spectral feature design system. It will be shown that the infinite clipped versions of the first 16 optimal features derived from the Weighted Karhunen-Loeve Transform have excellent classification performance. Further signal processing by canonical analysis increases the compression ratio and retains the classification accuracy. The overall probability of correct classification is over 90% while providing for a reduced downlink data rate by a factor of 10

Video and image systems engineering education for the 21st century

Includes bibliographical references.We are developing a new graduate program at Purdue in Video and Image Systems Engineering (VISE). The project is comprised of three parts: a new curriculum centered around a degree option in VISE to be earned as part of the Masters or Ph.D. degrees; a state-of-the-art lecture/laboratory facility for instruction, laboratory experiments, and project and homework activities in VISE courses; and enhancement of existing courses and development of new courses in the VISE area.Supported by an Image Systems Engineering Grant from Hewlett-Packard Company

Analysis Research for Earth Resource Information Systems: Where Do We Stand?

For a decade or more research has been conducted which is intended to lead to the design of operational earth resource information systems. Clearly this research has not yet been completed. It does seem to be far enough along however, that an assessment relevant to future operational activities might well be possible and beneficial. In this paper a model operational system will be discussed not so much as a proposal for an actual system, but merely as a viable objective upon which to focus the research. Possible system configurations and constraints will be noted, and the trend in the cost for data processing will be discussed relative to such a system

An Analytical Approach to the Design of Spectral Measurements in the Design of Multispectral Sensor

The purpose of the research which led to this paper is to develop an analytical procedure for the design of the spectral channels for multispectral remote sensor systems. An optimum design based on the criterion of minimum mean-square representation error using the Karhunen-Loeve expansion was developed to represent the spectral response functions from a stratum. From the overall pattern recognition system perspective the effect of the representation accuracy on a typical performance criterion, the probability of correct classification, is investigated. Although the analytical technique was developed primarily for the purpose of sensor design it was found that the procedure has potential for making important contributions to scene understanding. It was concluded that spectral channels which have narrow bandwidths relative to current sensor systems may be necessary to provide adequate spectral representation and improved classification performance. The optimum sensor design provides a standard against which sub-optimum operational sensors can be compared