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An automated method for semantic classification of regions in coastal images

Author:
Hoonhout, B.M.   Radermacher, M.   Baart, F.   van der Maaten, L.J.P.  


Journal:
Coastal Engineering


Issue Date:
2015


Abstract(summary):

Highlights

We present a fully automated algorithm for coastal image classification.

Classification is based on 1727 features per pixel and structured learning.

The algorithm correctly classifies 93.0% of the pixels in an unseen dataset.

We illustrate the application of the algorithm with two case studies.

Dataset and software are provided as free and open-source data sources.

Abstract

Large, long-term coastal imagery datasets are nowadays a low-cost source of information for various coastal research disciplines. However, the applicability of many existing algorithms for coastal image analysis is limited for these large datasets due to a lack of automation and robustness. Therefore manual quality control and site- and time-dependent calibration are often required. In this paper we present a fully automated algorithm that classifies each pixel in an image given a pre-defined set of classes. The necessary robustness is obtained by distinguishing one class of pixels from another based on more than a thousand pixel features and relations between neighboring pixels rather than a handful of color intensities.

Using a manually annotated dataset of 192 coastal images, a SSVM is trained and tested to distinguish between the classes water, sand, vegetation, sky and object. The resulting model correctly classifies 93.0% of all pixels in a previously unseen image. Two case studies are used to show how the algorithm extracts beach widths and water lines from a coastal camera station.

Both the annotated dataset and the software developed to perform the model training and prediction are provided as free and open-source data sources.



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