Classification of closed vegetation types from the fusion of remote sensing images
Abstract
On-site monitoring of the Cerrado vegetation cover becomes impracticable due large coverage. The objective of this work was to perform the image fusion of the CBERS-4 satellite and to analyze the performance of the use of the fusion in the process of vegetation classification and soil occupation in the RPDS - Legado Verdes do Cerrado. The MUX sensors were selected to form the RGB false-color image and the PAN5m, followed by the substitution fusion process, using the IHS method. The results revealed that the fusion of images generated a gain of visual and spatial quality, improving the information used as a basis for classification. The thematic map had an overall performance of 84.83% and average confusion of 15.17% when using fusion, and 77.66% and 22.34% when using only image with RGB composition. On-site sampling contributed to the acquisition of the sample polygons and the correct definition of the classes, but the confounding values still considered high.
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How to cite
Rios, J. M.; Fonseca, C. D. S.; Santos, A. M. D.; Venturoli, F. (2019). Classification of closed vegetation types from the fusion of remote sensing images. TreeDimensional Journal, 04(07), 1-9. https://doi.org/10.18677/TreeDimensional_2019A2.
@article{rios2019,
title={Classification of closed vegetation types from the fusion of remote sensing images},
author={Rios, Jovan Martins and Fonseca, Carolinne de Sousa and Santos, Alex Mota dos and Venturoli, Fábio},
journal={TreeDimensional Journal},
year={2019},
volume={04},
number={07},
pages={1-9},
doi={10.18677/TreeDimensional_2019A2}
}TY - JOUR AU - Rios, Jovan Martins AU - Fonseca, Carolinne de Sousa AU - Santos, Alex Mota dos AU - Venturoli, Fábio TI - Classification of closed vegetation types from the fusion of remote sensing images JO - TreeDimensional Journal PY - 2019 VL - 04 IS - 07 SP - 1 EP - 9 DO - 10.18677/TreeDimensional_2019A2 AB - On-site monitoring of the Cerrado vegetation cover becomes impracticable due large coverage. The objective of this work was to perform the image fusion of the CBERS-4 satellite and to analyze the performance of the use of the fusion in the process of vegetation classification and soil occupation in the RPDS - Legado Verdes do Cerrado. The MUX sensors were selected to form the RGB false-color image and the PAN5m, followed by the substitution fusion process, using the IHS method. The results revealed that the fusion of images generated a gain of visual and spatial quality, improving the information used as a basis for classification. The thematic map had an overall performance of 84.83% and average confusion of 15.17% when using fusion, and 77.66% and 22.34% when using only image with RGB composition. On-site sampling contributed to the acquisition of the sample polygons and the correct definition of the classes, but the confounding values still considered high. KW - Remote Sensing KW - Fusion KW - CBERS 4 KW - Mapping KW - Cerrado ER -
Rios, J. M.; Fonseca, C. D. S.; Santos, A. M. D.; Venturoli, F. (2019). Classification of closed vegetation types from the fusion of remote sensing images. TreeDimensional Journal, 04(07), 1-9. https://doi.org/10.18677/TreeDimensional_2019A2. Import via Mendeley Web Importer using DOI 10.18677/TreeDimensional_2019A2
Add to Zotero using DOI 10.18677/TreeDimensional_2019A2 Rios, J. M.; Fonseca, C. D. S.; Santos, A. M. D.; Venturoli, F. (2019). Classification of closed vegetation types from the fusion of remote sensing images. TreeDimensional Journal, 04(07), 1-9. https://doi.org/10.18677/TreeDimensional_2019A2.
Rios, J. M.; Fonseca, C. D. S.; Santos, A. M. D.; Venturoli, F. (2019). Classification of closed vegetation types from the fusion of remote sensing images. TreeDimensional Journal, 04(07), 1-9. https://doi.org/10.18677/TreeDimensional_2019A2.