Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics
Distance/correlation metrics have emerged as a robust and simplified tool for assessing the spectral characteristics of hyperspectral image pixels and effectively categorizing vegetation within a specific study area. Correlation methods provide a readily deployable and computationally efficient appr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/14/7/1148 |
| _version_ | 1797590319507701760 |
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| author | Gabriel E. Chanchí Golondrino Manuel A. Ospina Alarcón Manuel Saba |
| author_facet | Gabriel E. Chanchí Golondrino Manuel A. Ospina Alarcón Manuel Saba |
| author_sort | Gabriel E. Chanchí Golondrino |
| collection | DOAJ |
| description | Distance/correlation metrics have emerged as a robust and simplified tool for assessing the spectral characteristics of hyperspectral image pixels and effectively categorizing vegetation within a specific study area. Correlation methods provide a readily deployable and computationally efficient approach, rendering them particularly advantageous for applications in developing nations or regions with limited resources. This article presents a comparative investigation of correlation/distance metrics for the identification of vegetation pixels in hyperspectral imagery. The study facilitates a comprehensive evaluation of five distance and/or correlation metrics, namely, direct correlation, cosine similarity, normalized Euclidean distance, Bray–Curtis distance, and Pearson correlation. Direct correlation and Pearson correlation emerged as the two metrics that demonstrated the highest accuracy in vegetation pixel identification. Using the selected methodologies, a vegetation detection algorithm was implemented and validated using a hyperspectral image of the Manga neighborhood in Cartagena de Indias, Colombia. The spectral library facilitated image processing, while the mathematical calculation of correlations was performed using the numpy and scipy libraries in the Python programming language. Both the approach adopted in this study and the implemented algorithm aim to serve as a point of reference for conducting detection studies on diverse material types in hyperspectral imagery using open-access programming platforms. |
| first_indexed | 2024-03-11T01:18:54Z |
| format | Article |
| id | doaj.art-c0fa06a2fb2b45c3a5c886a6c25bd74e |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-11T01:18:54Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-c0fa06a2fb2b45c3a5c886a6c25bd74e2023-11-18T18:16:11ZengMDPI AGAtmosphere2073-44332023-07-01147114810.3390/atmos14071148Vegetation Identification in Hyperspectral Images Using Distance/Correlation MetricsGabriel E. Chanchí Golondrino0Manuel A. Ospina Alarcón1Manuel Saba2Faculty of Engineering, University of Cartagena, Cartagena de Indias 130015, ColombiaFaculty of Engineering, University of Cartagena, Cartagena de Indias 130015, ColombiaFaculty of Engineering, University of Cartagena, Cartagena de Indias 130015, ColombiaDistance/correlation metrics have emerged as a robust and simplified tool for assessing the spectral characteristics of hyperspectral image pixels and effectively categorizing vegetation within a specific study area. Correlation methods provide a readily deployable and computationally efficient approach, rendering them particularly advantageous for applications in developing nations or regions with limited resources. This article presents a comparative investigation of correlation/distance metrics for the identification of vegetation pixels in hyperspectral imagery. The study facilitates a comprehensive evaluation of five distance and/or correlation metrics, namely, direct correlation, cosine similarity, normalized Euclidean distance, Bray–Curtis distance, and Pearson correlation. Direct correlation and Pearson correlation emerged as the two metrics that demonstrated the highest accuracy in vegetation pixel identification. Using the selected methodologies, a vegetation detection algorithm was implemented and validated using a hyperspectral image of the Manga neighborhood in Cartagena de Indias, Colombia. The spectral library facilitated image processing, while the mathematical calculation of correlations was performed using the numpy and scipy libraries in the Python programming language. Both the approach adopted in this study and the implemented algorithm aim to serve as a point of reference for conducting detection studies on diverse material types in hyperspectral imagery using open-access programming platforms.https://www.mdpi.com/2073-4433/14/7/1148distance-based methodurban vegetationvegetation clusteringvegetation classification |
| spellingShingle | Gabriel E. Chanchí Golondrino Manuel A. Ospina Alarcón Manuel Saba Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics Atmosphere distance-based method urban vegetation vegetation clustering vegetation classification |
| title | Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics |
| title_full | Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics |
| title_fullStr | Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics |
| title_full_unstemmed | Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics |
| title_short | Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics |
| title_sort | vegetation identification in hyperspectral images using distance correlation metrics |
| topic | distance-based method urban vegetation vegetation clustering vegetation classification |
| url | https://www.mdpi.com/2073-4433/14/7/1148 |
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