Applicability of data mining algorithms in the identification of beach features/patterns on high-resolution satellite data

• Tipo: Artigo em Revista Científica Internacional
• Data: 2015

Autores

Ana C Teodoro (Autor) FCUP

Revista

Título: Journal of Applied Remote Sensing

Vol. 9

Página Final: 095095

ISSN: 1931-3195

Editora: SPIE

Indexação

ISI Web of Knowledge - 11 Citações

Scopus - 12 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia do ambiente

Outras Informações

ID Authenticus: P-00A-62T

DOI: 10.1117/1.jrs.9.095095

Abstract (EN): The available beach classification algorithms and sediment budget models are mainly based on in situ parameters, usually unavailable for several coastal areas. A morphological analysis using remotely sensed data is a valid alternative. This study focuses on the application of data mining techniques, particularly decision trees (DTs) and artificial neural networks (ANNs) to an IKONOS-2 image in order to identify beach features/patterns in a stretch of the northwest coast of Portugal. Based on knowledge of the coastal features, five classes were defined. In the identification of beach features/patterns, the ANN algorithm presented an overall accuracy of 98.6% and a kappa coefficient of 0.97. The best DTs algorithm (with pruning) presents an overall accuracy of 98.2% and a kappa coefficient of 0.97. The results obtained through the ANN and DTs were in agreement. However, the ANN presented a classification more sensitive to rip currents. The use of ANNs and DTs for beach classification from remotely sensed data resulted in an increased classification accuracy when compared with traditional classification methods. The association of remotely sensed high-spatial resolution data and data mining algorithms is an effective methodology with which to identify beach features/patterns. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 19