A Soft Computing Approach for Selecting and Combining Spectral Bands
We introduce a soft computing approach for automatically selecting and combining indices from remote sensing multispectral images that can be used for classification tasks. The proposed approach is based on a Genetic-Programming (GP) framework, a technique successfully used in a wide variety of opti...
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| Veröffentlicht in: | Remote sensing (Basel, Switzerland) Switzerland), 2020-07, Vol.12 (14), p.2267, Article 2267 |
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| creator | Albarracin, Juan F. H. Oliveira, Rafael S. Hirota, Marina dos Santos, Jefersson A. Torres, Ricardo da S. |
| description | We introduce a soft computing approach for automatically selecting and combining indices from remote sensing multispectral images that can be used for classification tasks. The proposed approach is based on a Genetic-Programming (GP) framework, a technique successfully used in a wide variety of optimization problems. Through GP, it is possible to learn indices that maximize the separability of samples from two different classes. Once the indices specialized for all the pairs of classes are obtained, they are used in pixelwise classification tasks. We used the GP-based solution to evaluate complex classification problems, such as those that are related to the discrimination of vegetation types within and between tropical biomes. Using time series defined in terms of the learned spectral indices, we show that the GP framework leads to superior results than other indices that are used to discriminate and classify tropical biomes. |
| doi_str_mv | 10.3390/rs12142267 |
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H. ; Oliveira, Rafael S. ; Hirota, Marina ; dos Santos, Jefersson A. ; Torres, Ricardo da S.</creator><creatorcontrib>Albarracin, Juan F. H. ; Oliveira, Rafael S. ; Hirota, Marina ; dos Santos, Jefersson A. ; Torres, Ricardo da S.</creatorcontrib><description>We introduce a soft computing approach for automatically selecting and combining indices from remote sensing multispectral images that can be used for classification tasks. The proposed approach is based on a Genetic-Programming (GP) framework, a technique successfully used in a wide variety of optimization problems. Through GP, it is possible to learn indices that maximize the separability of samples from two different classes. Once the indices specialized for all the pairs of classes are obtained, they are used in pixelwise classification tasks. We used the GP-based solution to evaluate complex classification problems, such as those that are related to the discrimination of vegetation types within and between tropical biomes. 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| subjects | Algorithms Classification Ecosystems Environmental Sciences Environmental Sciences & Ecology genetic programming Geology Geosciences, Multidisciplinary Grasslands Image classification Imaging Science & Photographic Technology Life Sciences & Biomedicine Optimization Physical Sciences Remote Sensing Science & Technology Sensors Soft computing Spectral bands spectral indices Task complexity Technology Vegetation vegetation indices |
| title | A Soft Computing Approach for Selecting and Combining Spectral Bands |
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