Paper
24 October 2013 The use of decision trees in the classification of beach forms/patterns on IKONOS-2 data
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Abstract
Evaluation of beach hydromorphological behaviour and its classification is highly complex. The available beach morphologic and classification models are mainly based on wave, tidal and sediment parameters. Since these parameters are usually unavailable for some regions – such as in the Portuguese coastal zone - a morphologic analysis using remotely sensed data seems to be a valid alternative. Data mining for spatial pattern recognition is the process of discovering useful information, such as patterns/forms, changes and significant structures from large amounts of data. This study focuses on the application of data mining techniques, particularly Decision Trees (DT), to an IKONOS-2 image in order to classify beach features/patterns, in a stretch of the northwest coast of Portugal. Based on the knowledge of the coastal features, five classes were defined: Sea, Suspended-Sediments, Breaking-Zone, Beachface and Beach. The dataset was randomly divided into training and validation subsets. Based on the analysis of several DT algorithms, the CART algorithm was found to be the most adequate and was thus applied. The performance of the DT algorithm was evaluated by the confusion matrix, overall accuracy, and Kappa coefficient. In the classification of beach features/patterns, the algorithm presented an overall accuracy of 98.2% and a kappa coefficient of 0.97. The DTs were compared with a neural network algorithm, and the results were in agreement. The methodology presented in this paper provides promising results and should be considered in further applications of beach forms/patterns classification.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
A. C. Teodoro, D. Ferreira, and H. Gonçalves "The use of decision trees in the classification of beach forms/patterns on IKONOS-2 data", Proc. SPIE 8893, Earth Resources and Environmental Remote Sensing/GIS Applications IV, 88930N (24 October 2013); https://doi.org/10.1117/12.2029212
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Cited by 1 scholarly publication.
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KEYWORDS
Data mining

Earth observing sensors

High resolution satellite images

Remote sensing

Visualization

Accuracy assessment

Algorithm development

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