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Research and Development of Algorithms for Change Detection in Remote Sensing Imagery


Remote Sensing has become an important tool for environmental processes analysis. Among several applications, change detection using Remote Sensing imagery is a topic of great interest. Perform this application in a temporal and accurate way is extremely important to understand the relations between anthropic and natural phenomena, allowing then better decision making. Use image classification as an intermediate step for change detection has been shown as an appropriate procedure. However, this approach depends on the accuracy of the classification results, which consequently motivates the development of more accurate classification methods. Based on the concept of divergence, stochastic distances have received great attention in recent years. In the light of this discussion, this project proposes the development of new change detection methods with basis on concepts derived from classification and stochastic distance able to produce robust and competitive results in comparison to conventional methodologies. Analysis with simulated data and practical applications will be carried in order to validate proposed methods. (AU)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
SILVA ROTTA, LUIZ HENRIQUE; ALCANTARA, ENNER; PARK, EDWARD; NEGRI, ROGERIO GALANTE; LIN, YUNUNG NINA; BERNARDO, NARIANE; GONCALVES MENDES, TATIANA SUSSEL; SOUZA FILHO, CARLOS ROBERTO. The 2019 Brumadinho tailings dam collapse: Possible cause and impacts of the worst human and environmental disaster in Brazil. International Journal of Applied Earth Observation and Geoinformation, v. 90, AUG 2020. Web of Science Citations: 0.
SAPUCCI, GABRIELA RIBEIRO; NEGRI, ROGERIO GALANTE. Hierarchical clustering and stochastic distance for indirect semi-supervised remote sensing image classification. SN APPLIED SCIENCES, v. 1, n. 3 MAR 2019. Web of Science Citations: 0.

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