CREATION OF SOIL PERMEABILITY MAPS TROUGH OBIA CLASSIFICATION OF VERY HIGH-RESOLUTION SATELLITE IMAGES
In the last few months, we have been working on images acquired by the WorldView3 satellite over the city of Pavia with the final intent to create a soil permeability map. These maps can be particularly useful in various fields, such as water management and public green, for evaluate the correlation...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-05-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2022/159/2022/isprs-archives-XLIII-B3-2022-159-2022.pdf |
| Summary: | In the last few months, we have been working on images acquired by the WorldView3 satellite over the city of Pavia with the final intent to create a soil permeability map. These maps can be particularly useful in various fields, such as water management and public green, for evaluate the correlation between overbuilt areas and pollution, the influence of vegetation on the temperature in within the different areas of the city, for the planning and monitoring of a sustainable transition of cities. To create such maps, it is essential to be able to identify various objects lying in the images, in our case we have done a classification of the image using the software Trimble eCognition™, applying Object-based Image Analysis (OBIA) approach and various classification methods, by applying fuzzy logic and supervised classification. The objects generated through various segmentations have been classified into 7 classes, water, fields, cultivated fields / low vegetation, high vegetation, roads, red roofs, and white roofs. And from the comparison with the manually defined ground truth, an overall accuracy degree of 80% was achieved. Furthermore, by applying various aggregation strategies, by combining the cultivated fields / low vegetation and high vegetation classes, we achieved a better overall accuracy of 91%. |
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| ISSN: | 1682-1750 2194-9034 |