Enteromorpha detection in aerial images using support vector machines

In this paper, we introduce a simple approach for detecting enteromorpha based on statistical learning of image features using support vector machines (SVM). The approach first classifies an enteromorpha image into two classes: enteromorpha and background. Then it extracts features from those two classes and uses them for training the SVM model. Finally, the predicting process is carried out in a pixel by pixel manner using the learned model. The model uses saturation in NTSC color space or filtered images by Gabor filter as the input features while the output class label is treated as 1 or 2 (enteromorpha or background), which is assigned to the location that is being predicted. In fact, this application is only a two-class pattern classification problem. Experimental results show that the method can be effectively applied to detecting enteromorpha in aerial images.