Extended Averaged Learning Subspace Method for Hyperspectral Data Classification

Averaged learning subspace methods (ALSM) have the advantage of being easily implemented and appear to outperform in classification problems of hyperspectral images. However, there remain some open and challenging problems, which if addressed, could further improve their performance in terms of classification accuracy. We carried out experiments mainly by using two kinds of improved subspace methods (namely, dynamic and fixed subspace methods), in conjunction with the [0,1] and [-1,+1] normalization methods. We used different performance indicators to support our experimental studies: classification accuracy, computation time, and the stability of the parameter settings. Results are presented for the AVIRIS Indian Pines data set. Experimental analysis showed that the fixed subspace method combined with the [0,1] normalization method yielded higher classification accuracy than other subspace methods. Moreover, ALSMs are easily applied: only two parameters need to be set, and they can be applied directly to hyperspectral data. In addition, they can completely identify training samples in a finite number of iterations

Modifed Minimum Classification Error Learning and Its Application to Neural Networks

A novel method to improve the generalization performance of the Minimum Classification Error (MCE) / Generalized Probabilistic Descent (GPD) learning is proposed. The MCE/GPD learning proposed by Juang and Katagiri in 1992 results in better recognition performance than the maximum-likelihood (ML) based learning in various areas of pattern recognition. Despite its superiority in recognition performance, as well as other learning algorithms, it still suffers from the problem of "over-fitting" to the training samples. In the present study, a regularization technique has been employed to the MCE learning to overcome this problem. Feed-forward neural networks are employed as a recognition platform to evaluate the recognition performance of the proposed method. Recognition experiments are conducted on several sorts of data sets

A Study on the Recognition of Seabed Environments Employing Sonar Images

The ocean accounts for approximately 70% of the area on the earth, and the water as well as coastal areas sustain many species including humans. Ocean resources are used for fish farming, land reclamation, and a variety of other purposes. Seabed resources such as oil, natural gas methane hydrates, and manganese nodules are still largely unexploited on the bottom of the sea. Maps are critical to development activities such as construction, mining, offshore drilling, marine traffic control, security, environmental protection, and tourism. Accordingly, more topographic and others types of mapping information are needed for marine and submarine investigations. Both waterborne and airborne survey techniques show promise for collecting data on marine and submarine environments, and these techniques can be classified into four main categories. First, remote sensing by satellites or aircraft is a widely used technique that can yield important data such as information on sea levels and coastal sediment transport. Second, investigations may collect direct information by remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), and divers. While the quality of data obtained from these techniques is high, the data obtained are often limited to relatively shallow and small geographic areas. Third, sediment profile imagery can be used to collect photographs that contain detailed information about the seabed. Lastly, acoustic investigations that use sonar are popular in marine mapping studies, especially in coastal areas. In particular, acoustic investigations that employ ultrasound technology can yield rich information about variations in bathymetry. Unlike air, water has physical properties that make it difficult for light or electromagnetic waves to pass through. However, sound waves propagate readily in water. Therefore, sound waves are used in a wide range of technical applications to detect underwater structures that are difficult to observe with light-based techniques. In the dark depths of the ocean, the use of acoustic technology is essential. The development of marine acoustic technology is expanding in modern times. In addition to the basic physics related to acoustic waves, much research has been dedicated to other basic and applied fields such as electronics, physical oceanography, signal processing, and biology. The realization of new sonar systems that utilize advanced detection algorithms can be expected to contribute to major breakthroughs in oceanographic research that require deployment to novel marine environments and other areas of natural resource interest. In this study, the author focuses on side-scan sonar, which is one of the imaging technologies that employs sound to determine the seabed state, to conduct research on imaging algorithms for discrimination. The proposed method for discrimination was coupled to a high-speed detection method for installed reefs on the seabed. This method is also capable of detecting unknown objects with Haar-like features during object recognition of rectangular regions of a certain size via machine learning by AdaBoost and fast elimination of non-object regions on the cascade structure. Side-scan and forward looking sonars are some of the most widely used imaging systems for obtaining large-scale images of the seafloor, and their application continues to expand rapidly with their increasing deployment on AUVs. However, it can be difficult to extract quantitative information from the images generated from these processes, in particular, for the detection and extraction of information on the objects within these images. Hence, this study analyzes features that are common to most undersea objects projected in side-scan sonar images to improve information processing. By using a technique based on the k-means method to determine the Haar-like features, the number of patterns of Haar-like features was minimized and the proposed method was capable of detecting undersea objects faster than current methodology. This study demonstrates the effectiveness of this method by applying it to the detection of real objects imaged on the seabed (i.e., sandy ground and muddy ground). Attempts are made as well to automate the proposed method for discriminating objects lying on the seafloor from surficial sediments. During undersea exploration, a thorough understanding of the state of the seafloor surrounding objects of interest is important. Therefore, a method is proposed in this study to automatically determine seabed sediment characteristics. In traditional methods, a variety of techniques have been used to collect information about seabed sediments including depth measurements, bathymetry evaluations, and seabed image analyses using the co-occurrence direction of the gray values of the image. Unfortunately, such data cannot be estimated from the object image itself and it can take a long time to obtain the required information. Therefore, these techniques are not currently suitable for real-time identification of objects on the seafloor. For practical purposes, automatic techniques that are developed should follow a simple procedure that results in highly precise and accurate classifications. The technique proposed here uses the subspace method, which is a method that has been used for supervised pattern recognition and analyses of higher-order local autocorrelation features. The most important feature of this method is that it uses only acoustic images obtained from the side-scan sonar. This feature opens up the possibility of installing this technology in unmanned small digital devices. In this study, the classification accuracy of the proposed automation method is compared to the accuracy of traditional methods in order to show the usefulness of the technology. In addition, the proposed method is applied to real-world images of the seabed to evaluate its effectiveness in marine surveys. The thesis is organized as follows. In Chapter 1, the purpose of this study is presented and previous studies relevant to this research are reviewed. In Chapter 2, an overview of underwater sound is given and key principles of sound wave technology are explained. In Chapter 3, a new method for detecting and discriminating objects on the seafloor is proposed. In Chapter 4, the possibility of automating the discrimination method is explored. Finally, Chapter 5 summarizes the findings of this study and proposes new avenues for future research.九州工業大学博士学位論文 学位記番号:工博甲第364号　学位授与年月日:平成26年3月25日Chapter 1 Introduction|Chapter 2 Underwater acoustics|Chapter 3 Detection of underwater objects based on machine learning|Chapter 4 Automatic classification of seabed sediments using HLAC|Chapter 5 Conclusion九州工業大学平成25年

A Study on the Recognition of Seabed Environments Employing Sonar Images

The ocean accounts for approximately 70% of the area on the earth, and the water as well as coastal areas sustain many species including humans. Ocean resources are used for fish farming, land reclamation, and a variety of other purposes. Seabed resources such as oil, natural gas methane hydrates, and manganese nodules are still largely unexploited on the bottom of the sea. Maps are critical to development activities such as construction, mining, offshore drilling, marine traffic control, security, environmental protection, and tourism. Accordingly, more topographic and others types of mapping information are needed for marine and submarine investigations. Both waterborne and airborne survey techniques show promise for collecting data on marine and submarine environments, and these techniques can be classified into four main categories. First, remote sensing by satellites or aircraft is a widely used technique that can yield important data such as information on sea levels and coastal sediment transport. Second, investigations may collect direct information by remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), and divers. While the quality of data obtained from these techniques is high, the data obtained are often limited to relatively shallow and small geographic areas. Third, sediment profile imagery can be used to collect photographs that contain detailed information about the seabed. Lastly, acoustic investigations that use sonar are popular in marine mapping studies, especially in coastal areas. In particular, acoustic investigations that employ ultrasound technology can yield rich information about variations in bathymetry. Unlike air, water has physical properties that make it difficult for light or electromagnetic waves to pass through. However, sound waves propagate readily in water. Therefore, sound waves are used in a wide range of technical applications to detect underwater structures that are difficult to observe with light-based techniques. In the dark depths of the ocean, the use of acoustic technology is essential. The development of marine acoustic technology is expanding in modern times. In addition to the basic physics related to acoustic waves, much research has been dedicated to other basic and applied fields such as electronics, physical oceanography, signal processing, and biology. The realization of new sonar systems that utilize advanced detection algorithms can be expected to contribute to major breakthroughs in oceanographic research that require deployment to novel marine environments and other areas of natural resource interest. In this study, the author focuses on side-scan sonar, which is one of the imaging technologies that employs sound to determine the seabed state, to conduct research on imaging algorithms for discrimination. The proposed method for discrimination was coupled to a high-speed detection method for installed reefs on the seabed. This method is also capable of detecting unknown objects with Haar-like features during object recognition of rectangular regions of a certain size via machine learning by AdaBoost and fast elimination of non-object regions on the cascade structure. Side-scan and forward looking sonars are some of the most widely used imaging systems for obtaining large-scale images of the seafloor, and their application continues to expand rapidly with their increasing deployment on AUVs. However, it can be difficult to extract quantitative information from the images generated from these processes, in particular, for the detection and extraction of information on the objects within these images. Hence, this study analyzes features that are common to most undersea objects projected in side-scan sonar images to improve information processing. By using a technique based on the k-means method to determine the Haar-like features, the number of patterns of Haar-like features was minimized and the proposed method was capable of detecting undersea objects faster than current methodology. This study demonstrates the effectiveness of this method by applying it to the detection of real objects imaged on the seabed (i.e., sandy ground and muddy ground). Attempts are made as well to automate the proposed method for discriminating objects lying on the seafloor from surficial sediments. During undersea exploration, a thorough understanding of the state of the seafloor surrounding objects of interest is important. Therefore, a method is proposed in this study to automatically determine seabed sediment characteristics. In traditional methods, a variety of techniques have been used to collect information about seabed sediments including depth measurements, bathymetry evaluations, and seabed image analyses using the co-occurrence direction of the gray values of the image. Unfortunately, such data cannot be estimated from the object image itself and it can take a long time to obtain the required information. Therefore, these techniques are not currently suitable for real-time identification of objects on the seafloor. For practical purposes, automatic techniques that are developed should follow a simple procedure that results in highly precise and accurate classifications. The technique proposed here uses the subspace method, which is a method that has been used for supervised pattern recognition and analyses of higher-order local autocorrelation features. The most important feature of this method is that it uses only acoustic images obtained from the side-scan sonar. This feature opens up the possibility of installing this technology in unmanned small digital devices. In this study, the classification accuracy of the proposed automation method is compared to the accuracy of traditional methods in order to show the usefulness of the technology. In addition, the proposed method is applied to real-world images of the seabed to evaluate its effectiveness in marine surveys. The thesis is organized as follows. In Chapter 1, the purpose of this study is presented and previous studies relevant to this research are reviewed. In Chapter 2, an overview of underwater sound is given and key principles of sound wave technology are explained. In Chapter 3, a new method for detecting and discriminating objects on the seafloor is proposed. In Chapter 4, the possibility of automating the discrimination method is explored. Finally, Chapter 5 summarizes the findings of this study and proposes new avenues for future research.九州工業大学博士学位論文 学位記番号:工博甲第364号　学位授与年月日:平成26年3月25日Chapter 1 Introduction|Chapter 2 Underwater acoustics|Chapter 3 Detection of underwater objects based on machine learning|Chapter 4 Automatic classification of seabed sediments using HLAC|Chapter 5 Conclusion九州工業大学平成25年

Anisotropic Harmonic Analysis and Integration of Remotely Sensed Data

This thesis develops the theory of discrete directional Gabor frames and several algorithms for the analysis of remotely sensed image data, based on constructions of harmonic analysis. The problems of image registration, image superresolution, and image fusion are separate but interconnected; a general approach using transform methods is the focus of this thesis. The methods of geometric multiresolution analysis are explored, particularly those related to the shearlet transform. Using shearlets, a novel method of image registration is developed that aligns images based on their shearlet features. Additionally, the anisotropic nature of the shearlet transform is deployed to smoothly superrsolve remotely-sensed image with edge features. Wavelet packets, a generalization of wavelets, are utilized for a flexible image fusion algorithm. The interplay between theoretical guarantees for these mathematical constructions, and their effectiveness for image processing is explored throughout

Aspects of learning within networks of spiking neurons

Spiking neural networks have, in recent years, become a popular tool for investigating the properties and computational performance of large massively connected networks of neurons. Equally as interesting is the investigation of the potential computational power of individual spiking neurons. An overview is provided of current and relevant research into the Liquid Sate Machine, biologically inspired artificial STDP learning mechanisms and the investigation of aspects of the computational power of artificial, recurrent networks of spiking neurons. First, it is shown that, using simple structures of spiking Leaky Integrate and Fire (LIF) neurons, a network n(P), can be built to perform any program P that can be performed by a general parallel programming language. Next, a form of STDP learning with normalisation is developed, referred to as STDP + N learning. The effects of applying this STDP + N learning within recurrently connected networks of neurons is then investigated. It is shown experimentally that, in very specific circumstances Anti-Hebbian and Hebbian STDP learning may be considered to be approximately equivalent processes. A metric is then developed that can be used to measure the distance between any two spike trains. The metric is then used, along with the STDP + N learning, in an experiment to examine the capacity of a single spiking neuron that receives multiple input spike trains, to simultaneously learn many temporally precise Input/Output spike train associations. The STDP +N learning is further modified for use in recurrent networks of spiking neurons, to give the STDP + NType2 learning methodology. An experiment is devised which demonstrates that the Type 2 method of applying learning to the synapses of a recurrent network — effectively a randomly shifting locality of learning — can enable the network to learn firing patterns that the typical application of learning is unable to learn. The resulting networks could, in theory, be used to create to simple structures discussed in the first chapter of original work.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Applications of hybrid and digital computation methods in aerospace-related sciences and engineering

The computing equipment in the engineering systems simulation laboratory of the Houston University Cullen College of Engineering is described and its advantages are summarized. The application of computer techniques in aerospace-related research psychology and in chemical, civil, electrical, industrial, and mechanical engineering is described in abstracts of 84 individual projects and in reprints of published reports. Research supports programs in acoustics, energy technology, systems engineering, and environment management as well as aerospace engineering