Abstract
This paper presents the comparisons of three soft classification methods and three sub-pixel mapping methods for the classification of coastal areas at sub-pixel level. Specifically, SPOT-7 multispectral images covering the coastal area of Perth are selected as the experiment dataset. For the soft classification, linear spectral unmixing model, supervised fully-fuzzy classification method and the support vector machine are applied to generate the fraction map. Then for the sub-pixel mapping, the sub-pixel/pixel attraction model, pixel swapping and wavelets method are compared. Besides, the influence of the correct fraction constraint is explored. Moreover, a post-processing step is implemented according to the known spatial knowledge of coastal areas. The accuracy assessment of the fraction values indicates that support vector machine generates the most accurate fraction result. For sub-pixel mapping, wavelets method outperforms the other two methods with overall classification accuracy of 91.79% and Kappa coefficient of 0.875 after the post-processing step and it also performs best for waterline extraction with mean distance of 0.71m to the reference waterline. In this experiment, the use of correct fraction constraint decreases the classification accuracy of sub-pixel mapping methods and waterline extraction. Finally, the post-processing step improves the accuracy of sub-pixel mapping methods, especially for those with correct coefficient constraint. The most significant improvement of overall accuracy is as much as 4% for the sub-pixel/pixel attraction model with correct coefficient constraint.
Highlights
Coastal image classification is important for the monitoring of changes of coastal features, such as shoreline position and the coverages of coastal water bodies, sandy beaches and vegetation
Sub-pixel/pixel Attraction Model (SAM) correct fraction (CF) and Artificial Neural Networks (ANN) Wavelet transform (WT) CF are comparable with HC result after postprocessing, which indicates that the methods with CF constraint can be potentially improved with proper post-processing
A set of SPOT-7 multispectral images is used to test the performance of soft classification methods and sub-pixel mapping methods for the classification of coastal areas in subpixel level
Summary
Coastal image classification is important for the monitoring of changes of coastal features, such as shoreline position and the coverages of coastal water bodies, sandy beaches and vegetation. Heremans and Van Orshoven (2015) compared 6 commonly applied machine learning methods, i.e. Multilayer Perception, Support Vector Regression, the Least-Squares (LS)SVM, Bagged Regression Trees, Boosted Regression Trees and Random Forests for sub-pixel land cover classification with 8day MODIS NDVI images based on multiple criteria. They found that SVMs outperform other methods when time and training data are not considered. The double-blind peer-review was conducted on the basis of the full paper
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