Long-term learning for adaptive underwater UXO classification
| dc.contributor.author | Hall, John Joseph, author | |
| dc.contributor.author | Azimi-Sadjadi, Mahmood R., advisor | |
| dc.contributor.author | Pezeshki, Ali, committee member | |
| dc.contributor.author | Luo, J. Rockey, committee member | |
| dc.contributor.author | Kirby, Michael, committee member | |
| dc.date.accessioned | 2022-05-30T10:22:37Z | |
| dc.date.available | 2022-05-30T10:22:37Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Classification of underwater objects such as unexploded ordnances (UXO) and mines from sonar datasets poses a difficult problem. Among factors that complicate classification of these objects are: variations in the operating and environmental conditions, presence of spatially varying clutter, variations in target shape, composition, orientation and burial conditions. Furthermore, collection of large quantities of real and representative data for training and testing in various background conditions is very difficult and impractical in many cases. In this dissertation, we build on our previous work in [1] where sparse-reconstruction based classification models were trained on synthetically generated sonar datasets to perform classification on real datasets. While this earlier work helped address issues of data poverty that are intrinsic to the underwater mine-hunting problem, in this work we change course to focus on the adaptation of such models. Particularly, we investigate approaches to adapting linear and kernelized forms of sparse reconstruction based classifiers (SRCs) to function in a lifelong learning setting in order to perform classification as environmental parameters are constantly evolving, without sacrificing performance on previously encountered environments. In this dissertation, we try to address several key questions for designing robust classifiers for UXO and munitions classification from low frequency sonar in a Lifelong learning setting. These include: (1) What are the most viable mechanisms to allow an unmanned underwater vehicle to accumulate and incorporate novel labeled or un-labeled data into its target identification system without sacrificing performance in old environments? (2) What are the most viable mechanisms for allowing an underwater ATR system to extract class labels despite varying environmental conditions? (3) What are the advantages, shortcomings, and major differences, of compressed-sensing based approaches to target identification, such as the modified MSC with incremental dictionaries, versus popular alternatives such as multi-task learning approaches? (4) How can the modified MSC framework from [1, 2] be extended to allow for kernelized solutions in an efficient manner? In this work, we propose several novel algorithms in order to address the problems of kernelizing compressed-sensing systems and transitioning these systems to an efficient incremental learning that does not depend on the full kernel matrix of all training samples. By kernelizing the sparse reconstruction classifier, the benefits of: sparse representations and non-linear embedding of samples can be coupled. Among the novel algorithms presented in this dissertation include: an incremental linearized kernel embedding (LKE) that leverages Nystrom approximation [3–5] for useful geometric interpretation in the embedded space; A novel algorithm for updating the eigen-decomposition of a growing kernel matrix which leverages fast arrowhead matrix eigendecompositions; and a method for optimizing a custom kernel function for M-ary discrimination tasks. A major technical question that is addressed in this work pertains to whether or not the Matched Subspace Classifier (MSC) [2, 6] can successfully be kernelized and converted into an adaptive form for use in a lifelong learning setting. The comprehensive testing of the incremental kernelized MSC and its application to the classification of munitions using low frequency sonar is another primary objective of this work. To this end, we test the hypothesis that the non-linearly mapped spectral features captured in the acoustic color (AC) data [2,7,8], extracted from the sonar back-scattered from various objects, display unique features providing superior discrimination between different classes of detected objects to the standard features. In this dissertation we present new classification results using three variants of a kernelized MSC, including an incremental linearized kernel embedding (LKE) MSC with uniform and ridge-leverage score (RLS) sampling, along with an incremental version of the linear version of the modified MSC from [2]. These classifier systems are applied to real sonar datasets, namely the TREX13 and PondEX09-10, to test the generalization ability of classifiers whose baseline training is performed on synthetic (i.e model generated) sonar datasets generated by a fast ray model (FRM), also known as the Target in environment response (TIER) model [8, 9]. In the incremental cases, a very limited number of labeled samples are utilized to augment the signal models when moving into a new operating environment. The methods presented here have provided extremely promising results so far, with the incremental LKE based MSC system providing PCC = 94.6%, PFA = 5.4%, and PCC = 99.3%, PFA = 0.7%, when using seven aspects (AC features) per decision, for the TREX13 and PondEX09-10 respectively. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | Hall_colostate_0053A_17082.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/235299 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | eigenvalue decomposition | |
| dc.subject | lifelong learning | |
| dc.subject | adaptive learning | |
| dc.subject | long-term learning | |
| dc.subject | kernel learning | |
| dc.title | Long-term learning for adaptive underwater UXO classification | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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