A machine learning strategy based on Kittler's taxonomy to detect anomalies and recognize contexts applied to monitor water bodies in environments
Authors
Dias, Mauricio Araújo; Marinho, Giovanna Carreira; Negri, Rogerio Galante; Casaca, Wallace; Bravo Muñoz, Ignacio; [et al.]Identifiers
Permanent link (URI): http://hdl.handle.net/10017/60010DOI: 10.3390/rs14092222
ISSN: 2072-4292
Publisher
MDPI
Date
2022-05-06Bibliographic citation
Dias, M.A. [et al.], 2022, "A machine learning strategy based on Kittler's taxonomy to detect anomalies and recognize contexts applied to monitor water bodies in environments", Remote sensing, vol. 14, no. 9, art. no. 2222, pp. 1-38.
Keywords
Remote sensing
Kittler's taxonomy
Anomaly detection
Machine learning
Time series
Pattern recognition
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.3390/rs14092222Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2022 The authors
Access rights
info:eu-repo/semantics/openAccess
Abstract
Environmental monitoring, such as analyses of water bodies to detect anomalies, is recog nized worldwide as a task necessary to reduce the impacts arising from pollution. However, the large
number of data available to be analyzed in different contexts, such as in an image time series acquired
by satellites, still pose challenges for the detection of anomalies, even when using computers. This
study describes a machine learning strategy based on Kittler’s taxonomy to detect anomalies related
to water pollution in an image time series. We propose this strategy to monitor environments, detect ing unexpected conditions that may occur (i.e., detecting outliers), and identifying those outliers in
accordance with Kittler’s taxonomy (i.e., detecting anomalies). According to our strategy, contextual
and non-contextual image classifications were semi-automatically compared to find any divergence
that indicates the presence of one type of anomaly defined by the taxonomy. In our strategy, models
built to classify a single image were used to classify an image time series due to domain adaptation.
The results 99.07%, 99.99%, 99.07%, and 99.53% were achieved by our strategy, respectively, for
accuracy, precision, recall, and F-measure. These results suggest that our strategy allows computers
to recognize contexts and enhances their capabilities to solve contextualized problems. Therefore, our
strategy can be used to guide computational systems to make different decisions to solve a problem
in response to each context. The proposed strategy is relevant for improving machine learning, as
its use allows computers to have a more organized learning process. Our strategy is presented with
respect to its applicability to help monitor environmental disasters. A minor limitation was found in
the results caused by the use of domain adaptation. This type of limitation is fairly common when
using domain adaptation, and therefore has no significance. Even so, future work should investigate
other techniques for transfer learning
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