Unsupervised automatic target generation process via compressive sensing
| dc.contributor.author | Bekit, Adam | |
| dc.contributor.author | Porta, Charles Della | |
| dc.contributor.author | Lampe, Bernard | |
| dc.contributor.author | Xue, Bai | |
| dc.contributor.author | Chang, Chen-I | |
| dc.date.accessioned | 2019-10-03T14:51:50Z | |
| dc.date.available | 2019-10-03T14:51:50Z | |
| dc.date.issued | 2019-05-13 | |
| dc.description | SPIE Defense + Commercial Sensing, 2019, Baltimore, Maryland, United States. | en_US |
| dc.description.abstract | Unsupervised target generation for hyperspectral imagery (HSI) have generated great interest in the hyperspectral community. However, most of the current unsupervised target generation algorithms have to process large HSI data, which is acquired using the traditional Nyquist-Shannon sampling theorem, resulting in data with high band-to-band correlation. As a consequence, these algorithms end up processing redundant information, raising the demand for large memory storage, processing time, and transmission bandwidth. In the past, some efforts have been dedicated to dealing with the redundant information via data reduction (DR) or data compression post-acquisition. However, to the best of our knowledge, this challenge has been addressed outside the context of Compressive Sensing (CS). This paper applies CS data acquisition process at the sensor level so that the redundant information is removed at the early stage of the data processing chain. The main advantage of our approach is that it employs a random sensing process, and the concept of universality, to randomly sense the HSI bands and produce data containing the bare minimum information. We take advantage of CS Restricted Isometric Properties (RIP), Restricted Conformal Properties (RCP), and newly derived orthogonal sub-space projection (OSP) properties to perform automatic target generation process (ATGP) in the compressively sensed band domain (CSBD), instead of in the original data space (ODS), where the HSI data contains full spectral bands. Our experimental results show that, by working in the CSBD, we avoid processing redundant data and still maintain performance results that are comparable with the performance results obtained in the ODS. | en_US |
| dc.description.uri | https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10989/109890G/Unsupervised-automatic-target-generation-process-via--compressive-sensing/10.1117/12.2518359.full | en_US |
| dc.format.extent | 18 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.identifier | doi:10.13016/m2i2gv-jw2p | |
| dc.identifier.citation | Adam Bekit, Charles Della Porta, Bernard Lampe, Bai Xue, and Chen-I Chang "Unsupervised automatic target generation process via compressive sensing", Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 109890G (13 May 2019); https://doi.org/10.1117/12.2518359 | en_US |
| dc.identifier.uri | https://doi.org/10.1117/12.2518359 | |
| dc.identifier.uri | http://hdl.handle.net/11603/14968 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | SPIE | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | © 2019 Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. | |
| dc.subject | Compressive sensing (CS) | en_US |
| dc.subject | automatic target generation process (ATGP) | en_US |
| dc.subject | compressively sensed band domain (CSBD) | en_US |
| dc.subject | original data space (ODS) | en_US |
| dc.subject | orthogonal sub-space projection (OSP) | en_US |
| dc.subject | Restricted Orthogonal Sub-space Projection Property (ROSPP) | en_US |
| dc.title | Unsupervised automatic target generation process via compressive sensing | en_US |
| dc.type | Text | en_US |