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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.
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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