Full text
| |
| Author(s): |
Bruno Ferraz do Amaral
Total Authors: 1
|
| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2016-04-29 |
| Examining board members: |
Elaine Parros Machado de Sousa;
Gustavo Enrique de Almeida Prado Alves Batista;
Alexandre Plastino de Carvalho
|
| Advisor: | Elaine Parros Machado de Sousa |
| Abstract | |
The amount of digital data generated and stored as well as the need of creation and management of large databases has increased significantly, in the last decades. The possibility of finding valid and potentially useful patterns and information in large databases has attracted the attention of many scientific areas. Time series databases have been explored using data mining methods in serveral domains of application, such as economics, medicine and agrometeorology. Due to the large volume and complexity of some time series databases, the process of labeling data for supervised tasks, such as classification, can be very expensive. To overcome the problem of scarcity of labeled data, semi-supervised classification, which benefits from both labeled and unlabeled data available, can be applied to classify data from large time series databases. In this Master dissertation, we propose 1) a framework for the analysis of data extracted from satellite image time series (SITS) using data mining tasks and 2) a graph-based semi-supervised classification method, developed to classify temporal data obtained from satellite images. According to experts in agrometeorology, the use of the proposed method and framework provides an automatic way of analyzing data extracted from SITS, which is very useful for supporting research in this domain of application. We apply the framework and the proposed semi-supervised classification method in the analysis of vegetation index time series, aiming at identifying sugarcane crop fields, in Brazil. Experimental results indicate that our proposed framework is useful for supporting researches in agriculture, according to experts in the domain of application. We also show that our method is more accurate than traditional supervised methods and related semi-supervised methods. (AU) | |
| FAPESP's process: | 13/01615-9 - Semi-supervised classification of time series extracted from satellite images |
| Grantee: | Bruno Ferraz Do Amaral |
| Support Opportunities: | Scholarships in Brazil - Master |