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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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 12; no. 14; p. 2267
Main Authors Albarracín, Juan F. H., Oliveira, Rafael S., Hirota, Marina, dos Santos, Jefersson A., Torres, Ricardo da S.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2020
Subjects
Algorithms
Classification
Ecosystems
genetic programming
Grasslands
Image classification
Optimization
Remote sensing
Sensors
Soft computing
Spectral bands
spectral indices
Task complexity
Vegetation
vegetation indices
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs12142267