Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering
[EN] Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a yperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agg...
| Authors: | ; ; ; |
|---|---|
| Format: | Article |
| Publication date: | 2020 |
| Country: | España |
| Institution: | Universitat Politècnica de València (UPV) |
| Repository: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Language: | English |
| OAI Identifier: | oai:riunet.upv.es:10251/176146 |
| Online Access: | https://riunet.upv.es/handle/10251/176146 |
| Access Level: | Open access |
| Keyword: | Agglomerative clustering Principal component analysis Model-based clustering Independent component analysis TEORIA DE LA SEÑAL Y COMUNICACIONES |
| _version_ | 1878741354788421632 |
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| author | Prades Nebot, José|||0000-0003-4489-3704 |
| author2 | Salazar Afanador, Addisson|||0000-0001-5849-5104 Vergara Domínguez, Luís|||0000-0001-6803-4774 Safont Armero, Gonzalo |
| author2_role | author author author |
| author_browse | Prades Nebot, José|||0000-0003-4489-3704 Safont Armero, Gonzalo Salazar Afanador, Addisson|||0000-0001-5849-5104 Vergara Domínguez, Luís|||0000-0001-6803-4774 |
| author_facet | Prades Nebot, José|||0000-0003-4489-3704 Salazar Afanador, Addisson|||0000-0001-5849-5104 Vergara Domínguez, Luís|||0000-0001-6803-4774 Safont Armero, Gonzalo |
| author_role | author |
| collection | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| description | [EN] Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a yperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densities are estimated using Independent Component Analysis. Based on the K-means result, a model-based agglomerative clustering is performed, which provides a hierarchy of clusterings. Finally, a validation algorithm selects a clustering of the hierarchy; the number of clusters it contains is the estimated number of materials. Besides estimating the number of endmembers, the proposed method can approximately obtain the endmember (or spectrum) of each material by computing the centroid of its corresponding cluster. We have tested the proposed method using several hyperspectral images. The results show that the proposed method obtains approximately the number of materials that these images contain. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_e3b67a10186d9ee12bd00a5fbc6d5f98 |
| institution | Universitat Politècnica de València (UPV) |
| instname_str | Universitat Politècnica de València (UPV) |
| language | eng |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:riunet.upv.es:10251/176146 |
| publishDate | 2020 |
| publishDateSort | 2020 |
| publisher | MDPI AG |
| publisherStr | MDPI AG |
| reponame_str | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| spelling | Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering Prades Nebot, José|||0000-0003-4489-3704 Salazar Afanador, Addisson|||0000-0001-5849-5104 Vergara Domínguez, Luís|||0000-0001-6803-4774 Safont Armero, Gonzalo Agglomerative clustering Principal component analysis Model-based clustering Independent component analysis TEORIA DE LA SEÑAL Y COMUNICACIONES [EN] Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a yperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densities are estimated using Independent Component Analysis. Based on the K-means result, a model-based agglomerative clustering is performed, which provides a hierarchy of clusterings. Finally, a validation algorithm selects a clustering of the hierarchy; the number of clusters it contains is the estimated number of materials. Besides estimating the number of endmembers, the proposed method can approximately obtain the endmember (or spectrum) of each material by computing the centroid of its corresponding cluster. We have tested the proposed method using several hyperspectral images. The results show that the proposed method obtains approximately the number of materials that these images contain. This work was supported by Spanish Administration (Ministerio de Ciencia, Innovacion y Universidades) and European Union (FEDER) under grant TEC2017-84743-P. MDPI AG https://riunet.upv.es/handle/10251/176146 |
| spellingShingle | Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering Prades Nebot, José|||0000-0003-4489-3704 Agglomerative clustering Principal component analysis Model-based clustering Independent component analysis TEORIA DE LA SEÑAL Y COMUNICACIONES |
| title | Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering |
| title_full | Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering |
| title_fullStr | Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering |
| title_full_unstemmed | Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering |
| title_short | Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering |
| title_sort | Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering |
| topic | Agglomerative clustering Principal component analysis Model-based clustering Independent component analysis TEORIA DE LA SEÑAL Y COMUNICACIONES |
| topic_facet | Agglomerative clustering Principal component analysis Model-based clustering Independent component analysis TEORIA DE LA SEÑAL Y COMUNICACIONES |
| url | https://riunet.upv.es/handle/10251/176146 |