Anomalous behaviour detection using one-class support vector machine and remote sensing images: a case study of algal bloom occurrence in inland waters

• Published in: International journal of digital earth Vol. 14; no. 7; pp. 921 - 942
• Main Authors: Ananias, Pedro Henrique Moraes, Negri, Rogério Galante
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.07.2021; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: adverse effects; Algae; algal bloom detection; Algal blooms; anomalies; Case studies; China; Classifiers; Computer applications; Detection; Eutrophication; Ground truth; Image classification; Imagery; In situ measurement; Information processing; Inland waters; Internet; Lake Erie; Lakes; Landsat; Landsat satellites; Learning algorithms; Learning behaviour; Machine learning; Methods; Remote sensing; Satellite imagery; Spectra; spectral indices; Support vector machines; unsupervised classification; Lake Erie; China
• ISSN: 1753-8947; 1753-8955; 1753-8955
• DOI: 10.1080/17538947.2021.1907462

Algal blooms are a frequent subject in scientific discussions and are the focus of many recent studies, mainly due to their adverse effect on society. Given the lack of ground truth data and the need to develop tools for their detection and monitoring, this research proposes a novel method to automate detection. Concepts derived from multi-temporal image series processing, spectral indices and classification with One-class Support Vector Machine (OC-SVM) are used in this proposal. Imagery from multi-spectral sensors on Landsat-8 and MODIS were acquired through the Google Earth Engine API (GEE API). In order to evaluate our method, two bloom detection case studies (Lake Erie (USA) and Lake Taihu (China)) were performed. Comparisons were made with methods based on spectral index thresholds. Also, to demonstrate the performance of the OC-SVM classifier compared to other machine learning methods, the proposal was adapted to be used with a Random Forest (RF) classifier, having its results added to the analysis. In situ measurements show that the proposed method delivers highly accurate results compared to spectral index thresholding approaches. However, a drawback of the proposal refers to its higher computational cost. The application of the new method to a real-world bloom case is demonstrated.