Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns
Hyperspectral images (HSIs) are a powerful tool to classify the elements from an area of interest by their spectral signature. In this paper, we propose an efficient method to classify hyperspectral data using Voronoi diagrams and strong patterns in the absence of ground truth. HSI processing consum...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/19/5684 |
| _version_ | 1827704809698885632 |
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| author | Laura Bianca Bilius Ştefan Gheorghe Pentiuc |
| author_facet | Laura Bianca Bilius Ştefan Gheorghe Pentiuc |
| author_sort | Laura Bianca Bilius |
| collection | DOAJ |
| description | Hyperspectral images (HSIs) are a powerful tool to classify the elements from an area of interest by their spectral signature. In this paper, we propose an efficient method to classify hyperspectral data using Voronoi diagrams and strong patterns in the absence of ground truth. HSI processing consumes a great deal of computing resources because HSIs are represented by large amounts of data. We propose a heuristic method that starts by applying Parafac decomposition for reduction and to construct the abundances matrix. Furthermore, the representative nodes from the abundances map are searched for. A multi-partition of these nodes is found, and based on this, strong patterns are obtained. Then, based on the hierarchical clustering of strong patterns, an optimum partition is found. After strong patterns are labeled, we construct the Voronoi diagram to extend the classification to the entire HSI. |
| first_indexed | 2024-03-10T15:49:25Z |
| format | Article |
| id | doaj.art-184f99e1b90c4122a0dbd9ae57834ba8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T15:49:25Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-184f99e1b90c4122a0dbd9ae57834ba82023-11-20T16:07:48ZengMDPI AGSensors1424-82202020-10-012019568410.3390/s20195684Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong PatternsLaura Bianca Bilius0Ştefan Gheorghe Pentiuc1The Machine Intelligence and Information Visualization Lab (MintViz), Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies, and Distributed Systems for Fabrication and Control (MANSiD) Research Center, “Stefan cel Mare” University of Suceava, 720229 Suceava, RomaniaThe Machine Intelligence and Information Visualization Lab (MintViz), Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies, and Distributed Systems for Fabrication and Control (MANSiD) Research Center, “Stefan cel Mare” University of Suceava, 720229 Suceava, RomaniaHyperspectral images (HSIs) are a powerful tool to classify the elements from an area of interest by their spectral signature. In this paper, we propose an efficient method to classify hyperspectral data using Voronoi diagrams and strong patterns in the absence of ground truth. HSI processing consumes a great deal of computing resources because HSIs are represented by large amounts of data. We propose a heuristic method that starts by applying Parafac decomposition for reduction and to construct the abundances matrix. Furthermore, the representative nodes from the abundances map are searched for. A multi-partition of these nodes is found, and based on this, strong patterns are obtained. Then, based on the hierarchical clustering of strong patterns, an optimum partition is found. After strong patterns are labeled, we construct the Voronoi diagram to extend the classification to the entire HSI.https://www.mdpi.com/1424-8220/20/19/5684Voronoi diagramsParafac decompositionhyperspectral imagesclassificationabundances mapstrong patterns |
| spellingShingle | Laura Bianca Bilius Ştefan Gheorghe Pentiuc Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns Sensors Voronoi diagrams Parafac decomposition hyperspectral images classification abundances map strong patterns |
| title | Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns |
| title_full | Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns |
| title_fullStr | Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns |
| title_full_unstemmed | Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns |
| title_short | Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns |
| title_sort | efficient unsupervised classification of hyperspectral images using voronoi diagrams and strong patterns |
| topic | Voronoi diagrams Parafac decomposition hyperspectral images classification abundances map strong patterns |
| url | https://www.mdpi.com/1424-8220/20/19/5684 |
| work_keys_str_mv | AT laurabiancabilius efficientunsupervisedclassificationofhyperspectralimagesusingvoronoidiagramsandstrongpatterns AT stefangheorghepentiuc efficientunsupervisedclassificationofhyperspectralimagesusingvoronoidiagramsandstrongpatterns |