Comparison of linear and non-linear feature extraction on vegetation and oil spill hyperspectral images
| dc.creator | Ramirez-Aguilar, Andres | |
| dc.date | 2015-10-08T15:13:20Z | |
| dc.date | 2015-10-08T15:13:20Z | |
| dc.date | 2015-12 | |
| dc.description | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science from Texas A&M University-Corpus Christi in Corpus Christi, Texas. | |
| dc.description | A hyperspectral image provides a multidimensional figure rich in data consisting of hundreds of spectral dimensions. For this research, the method of analysis for a hyperspectral image will consist of two different feature extraction algorithms: principal component analysis locally linear embedding. Analyzing the spectral and spatial information of such image with linear and non-linear algorithms will result in high computational time. In order to overcome this problem, this research proposes a system using a MapReduce-Graphics Processing Unit (GPU) model that can help analyze a hyperspectral image through the usage of parallel hardware and a parallel programming model, which will be simpler to handle compared to other low level parallel programming models. Additionally, Hadoop will be used as an open-source version of the MapReduce parallel programming model. The ultimate goal of this research is to provide a foundation for a simple and powerful system that is scalable and easily extend-able. | |
| dc.description | Computing Sciences | |
| dc.description | College of Science and Engineering | |
| dc.identifier | http://hdl.handle.net/1969.6/645 | |
| dc.language | en_US | |
| dc.rights | This material is made available for use in research, teaching, and private study, pursuant to U.S. Copyright law. The user assumes full responsibility for any use of the materials, including but not limited to, infringement of copyright and publication rights of reproduced materials. Any materials used should be fully credited with its source. All rights are reserved and retained regardless of current or future development or laws that may apply to fair use standards. Permission for publication of this material, in part or in full, must be secured with the author and/or publisher. | |
| dc.subject | Hadoop | |
| dc.subject | GPU | |
| dc.subject | Feature Extraction | |
| dc.subject | locally linear embedding | |
| dc.subject | principal component analysis | |
| dc.subject | hyperspectral | |
| dc.title | Comparison of linear and non-linear feature extraction on vegetation and oil spill hyperspectral images | |
| dc.type | Text | |
| dc.type | Thesis |