Study of Enteromorpha Idendification Based on Machine Learning Technology

In this paper, we construct an enteromorpha remote sensing dataset. We take the eight channels data, from Band 1 to Band 8, in Landset 8 as spectral features of the dataset and consider the features extracted by Local Binary Patterns algorithm(LBP) and Gray-level Co-occurrence Matrix algorithm (GLCM) as texture features. Combining these two types of features, we create a novel machine learning dataset including enteromorpha, land, seawater, and cloud samples. Then we use the single-channel threshold method and the multi-channel ratio method to label the entire data set pixel-by-pixel based on the feature of spectral characteristics and ground reflection spectra. Four classifiers are trained via Support Vector Machine (SVM), K Nearest Neighbor (KNN), Expectation Maximization (EM) and Stack Autoencoders (SAE) on our dataset respectively. A new classifier with a set of weight coefficients, which is called weighted classifier, can be obtained by combining above classifiers according to the accuracy of the four classifiers on all kinds of samples. Experiments show that the classification accuracy of these classifiers in our dataset are higher than 90% and weighted classifier reaches 93.59%. If we only focus on enteromorpha data, the best classifier is KNN classifier which has the correct rate of 95.8%.