Interpretable land cover classification with modal decision trees
ABSTRACTLand cover classification (LCC) refers to the task of classifying each pixel in satellite/aerial imagery by predicting a label carrying information about its nature. Despite the importance of having transparent, symbolic decision models, in the recent literature, LCC has been mainly approach...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2023.2262738 |
| _version_ | 1797387310174568448 |
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| author | G. Pagliarini G. Sciavicco |
| author_facet | G. Pagliarini G. Sciavicco |
| author_sort | G. Pagliarini |
| collection | DOAJ |
| description | ABSTRACTLand cover classification (LCC) refers to the task of classifying each pixel in satellite/aerial imagery by predicting a label carrying information about its nature. Despite the importance of having transparent, symbolic decision models, in the recent literature, LCC has been mainly approached with black-box functional models, that are able to leverage the spatial dimensions within the data. In this article, we argue that standard symbolic decision models can be extended to perform a form of spatial reasoning that is adequate for LCC. We propose a generalization of a classical decision tree learning model, based on replacing propositional logic with a modal spatial logic, and provide a CART-like learning algorithm for it. We evaluate its performance at five different LCC tasks, showing that this technique leads to classification models whose performances are superior to those of their propositional counterpart, and at least comparable with those of non-symbolic ones. Ultimately, we show that spatial decision trees and random forests are able to extract complex, but interpretable spatial patterns. |
| first_indexed | 2024-03-08T22:22:21Z |
| format | Article |
| id | doaj.art-2c3ff7a1842c44dabccd3ff7044db7fc |
| institution | Directory Open Access Journal |
| issn | 2279-7254 |
| language | English |
| last_indexed | 2024-03-08T22:22:21Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj.art-2c3ff7a1842c44dabccd3ff7044db7fc2023-12-18T12:26:50ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542023-12-0156110.1080/22797254.2023.2262738Interpretable land cover classification with modal decision treesG. Pagliarini0G. Sciavicco1Department of Mathematics and Computer Science, University of Ferrara, Ferrara, ItalyDepartment of Mathematics and Computer Science, University of Ferrara, Ferrara, ItalyABSTRACTLand cover classification (LCC) refers to the task of classifying each pixel in satellite/aerial imagery by predicting a label carrying information about its nature. Despite the importance of having transparent, symbolic decision models, in the recent literature, LCC has been mainly approached with black-box functional models, that are able to leverage the spatial dimensions within the data. In this article, we argue that standard symbolic decision models can be extended to perform a form of spatial reasoning that is adequate for LCC. We propose a generalization of a classical decision tree learning model, based on replacing propositional logic with a modal spatial logic, and provide a CART-like learning algorithm for it. We evaluate its performance at five different LCC tasks, showing that this technique leads to classification models whose performances are superior to those of their propositional counterpart, and at least comparable with those of non-symbolic ones. Ultimately, we show that spatial decision trees and random forests are able to extract complex, but interpretable spatial patterns.https://www.tandfonline.com/doi/10.1080/22797254.2023.2262738Interpretable machine learningmodal logicdecision tree learninghyperspectral image classificationland useland cover |
| spellingShingle | G. Pagliarini G. Sciavicco Interpretable land cover classification with modal decision trees European Journal of Remote Sensing Interpretable machine learning modal logic decision tree learning hyperspectral image classification land use land cover |
| title | Interpretable land cover classification with modal decision trees |
| title_full | Interpretable land cover classification with modal decision trees |
| title_fullStr | Interpretable land cover classification with modal decision trees |
| title_full_unstemmed | Interpretable land cover classification with modal decision trees |
| title_short | Interpretable land cover classification with modal decision trees |
| title_sort | interpretable land cover classification with modal decision trees |
| topic | Interpretable machine learning modal logic decision tree learning hyperspectral image classification land use land cover |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2023.2262738 |
| work_keys_str_mv | AT gpagliarini interpretablelandcoverclassificationwithmodaldecisiontrees AT gsciavicco interpretablelandcoverclassificationwithmodaldecisiontrees |