Incorporating Multiresolution Analysis With Multiclassifiers And Decision Fusion For Hyperspectral Remote Sensing

The ongoing development and increased affordability of hyperspectral sensors are increasing their utilization in a variety of applications, such as agricultural monitoring and decision making. Hyperspectral Automated Target Recognition (ATR) systems typically rely heavily on dimensionality reduction methods, and particularly intelligent reduction methods referred to as feature extraction techniques. This dissertation reports on the development, implementation, and testing of new hyperspectral analysis techniques for ATR systems, including their use in agricultural applications where ground truthed observations available for training the ATR system are typically very limited. This dissertation reports the design of effective methods for grouping and down-selecting Discrete Wavelet Transform (DWT) coefficients and the design of automated Wavelet Packet Decomposition (WPD) filter tree pruning methods for use within the framework of a Multiclassifiers and Decision Fusion (MCDF) ATR system. The efficacy of the DWT MCDF and WPD MCDF systems are compared to existing ATR methods commonly used in hyperspectral remote sensing applications. The newly developed methods’ sensitivity to operating conditions, such as mother wavelet selection, decomposition level, and quantity and quality of available training data are also investigated. The newly developed ATR systems are applied to the problem of hyperspectral remote sensing of agricultural food crop contaminations either by airborne chemical application, specifically Glufosinate herbicide at varying concentrations applied to corn crops, or by biological infestation, specifically soybean rust disease in soybean crops. The DWT MCDF and WPD MCDF methods significantly outperform conventional hyperspectral ATR methods. For example, when detecting and classifying varying levels of soybean rust infestation, stepwise linear discriminant analysis, results in accuracies of approximately 30%-40%, but WPD MCDF methods result in accuracies of approximately 70%-80%

Incorporating Multiresolution Analysis With Multiclassifiers And Decision Fusion For Hyperspectral Remote Sensing

The ongoing development and increased affordability of hyperspectral sensors are increasing their utilization in a variety of applications, such as agricultural monitoring and decision making. Hyperspectral Automated Target Recognition (ATR) systems typically rely heavily on dimensionality reduction methods, and particularly intelligent reduction methods referred to as feature extraction techniques. This dissertation reports on the development, implementation, and testing of new hyperspectral analysis techniques for ATR systems, including their use in agricultural applications where ground truthed observations available for training the ATR system are typically very limited. This dissertation reports the design of effective methods for grouping and down-selecting Discrete Wavelet Transform (DWT) coefficients and the design of automated Wavelet Packet Decomposition (WPD) filter tree pruning methods for use within the framework of a Multiclassifiers and Decision Fusion (MCDF) ATR system. The efficacy of the DWT MCDF and WPD MCDF systems are compared to existing ATR methods commonly used in hyperspectral remote sensing applications. The newly developed methods’ sensitivity to operating conditions, such as mother wavelet selection, decomposition level, and quantity and quality of available training data are also investigated. The newly developed ATR systems are applied to the problem of hyperspectral remote sensing of agricultural food crop contaminations either by airborne chemical application, specifically Glufosinate herbicide at varying concentrations applied to corn crops, or by biological infestation, specifically soybean rust disease in soybean crops. The DWT MCDF and WPD MCDF methods significantly outperform conventional hyperspectral ATR methods. For example, when detecting and classifying varying levels of soybean rust infestation, stepwise linear discriminant analysis, results in accuracies of approximately 30%-40%, but WPD MCDF methods result in accuracies of approximately 70%-80%

Detecting anomalies in remotely sensed hyperspectral signatures via wavelet transforms

An automated subpixel target detection system has been designed and tested for use with remotely sensed hyperspectral images. A database of hyperspectral signatures was created to test the system using a variety of Gaussian shaped targets. The signal-to-noise ratio of the targets varied from -95dB to -50dB. The system utilizes a wavelet-based method (discrete wavelet transform) to extract an energy feature vector from each input pixel signature. The dimensionality of the feature vector is reduced to a one-dimensional feature scalar through the process of linear discriminant analysis. Signature classification is determined by nearest mean criterion that is used to assign each input signature to one of two classes, no target present or target present. Classification accuracy ranged from nearly 60% with target SNR at -95dB without any a priori knowledge of the target, to 100% with target SNR at -50dB and a priori knowledge about the location of the target within the spectral bands of the signature

Google the earth: what's next?

Sensing the Earth has proven to be a tremendously valuable tool for understanding the world around us. Over the last half-century, we have built a sophisticated network of satellites, aircraft, and ground-based remote sensing systems to provide raw information from which we derive and improve our knowledge of the Earth and its phenomena. Through remote sensing, our basic scientific knowledge of the Earth and how it functions has expanded rapidly in the last few decades. Applications of this knowledge, from natural hazard prediction to resource management, have already proven their benefit to society many times over. Today maps and satellite imageries have become an integral part of the developmental process and have also triggered new business opportunities. Maps are essential at all stages of infrastructure development, resource planning and the disaster management cycle. Satellite imagery/data can be used for everything from ground truthing and change detection, to more sophisticated analyses, including feature extraction and natural hazard prediction. As imagery has become more accessible and more affordable in recent years, there is also a growing convergence of imagery and geographic information system (GIS) applications. Geospatial scientists and analysts thus, need to be able to easily access imagery and move seamlessly between GIS and image processing applications to derive the most information possible from them. Technologically, the challenge is to design sensors that exhibit high sensitivity to the parameters of interest while minimizing instrument noise and impacts of other natural variables. The scientific challenge is to develop retrieval algorithms that describe the physical measurement process in sufficient detail, yet are simple enough to allow robust inversion of the remotely sensed signals. Considering the exponential growth of data volumes driven by the rapid progress in sensor and computer technologies in recent years, the future of remotely sensed data should ideally be in automated data processing, development of robust and transferable algorithms and processing chains that require little or no human intervention. In meeting the above mentioned challenges, some research works have been done at Universiti Putra Malaysia. These works cover all aspects of the remote sensing process, from instrument design, image processing, image analysis to the retrieval of geophysical parameters and their application in natural resources planning and disaster management. Some of the major research efforts include feature extraction from satellite imagery; spatial decision support system for oil spill detection, monitoring and contingency planning; fish forecasting; UAV-based remote imaging and natural disaster management and early warning systems for floods and landslides. This lecture concludes that through remote sensing, our basic scientific knowledge of the Earth and how it functions have expanded rapidly in the last few decades. Applications of this knowledge, from natural hazard prediction to resource management, have already proven to be beneficial to society many times over. As the demand for even faster, better and more temporally and spatially variable information grows dramatically, this lectures answers the question of what remote sensing will be like in the coming decades and the new capabilities and challenges that will emerg

The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies

This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Three dimensional quantitative textural analysis of nickel sulphide ore using X-ray computed tomography and grey level co-occurrence matrices on drill core

Alongside the global trend to mine and process lower grade and more mineralogically complex ores, there has been an increased awareness of the prevalence of ore heterogeneity. Ore texture - the interrelationship of minerals comprising a rock, has been identified as one of the primary geometallurgical indicators of ore variability. It is well known that a relationship exists between ore texture and the resultant metallurgical performance (ore hardness, throughput, liberation, grade, recovery). Consequently, there exists a need to rapidly, routinely, cost effectively, and reliably quantify ore texture and its variability prior to mining. This information can thereafter be incorporated into the geometallurgical block model and used for decision making informing mine planning, plant operation and optimisation, forecasting, and mine closure. The ability to rapidly, routinely, cost effectively and reliably quantify ore texture remains an ongoing challenge. In this study, the use of 3D X-ray computed tomography (XCT) is proposed as an innovative solution to non-destructively image the internal structure of drill core. Thereafter, an established, discipline independent two dimensional (2D) image analysis technique known as grey level co-occurrence matrices (GLCM) is specially adapted into three dimensions (3D) to quantify ore texture using XCT grey level volumes of drill core

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research

Level Set Methods for MRE Image Processing and Analysis

Ph.DDOCTOR OF PHILOSOPH

Proceedings of the NASA Conference on Space Telerobotics, volume 5

Papers presented at the NASA Conference on Space Telerobotics are compiled. The theme of the conference was man-machine collaboration in space. The conference provided a forum for researchers and engineers to exchange ideas on the research and development required for the application of telerobotics technology to the space systems planned for the 1990's and beyond. Volume 5 contains papers related to the following subject areas: robot arm modeling and control, special topics in telerobotics, telerobotic space operations, manipulator control, flight experiment concepts, manipulator coordination, issues in artificial intelligence systems, and research activities at the Johnson Space Center

Semester Courses and Course Equivalents: Graduate Courses Summary

A list comprised of summaries of all graduate courses and course equivalents at Wright State University