Combining magnitude and shape features for hyperspectral classification

The spectral angle mapper (SAM) and maximum likelihood classification (MLC) are two traditional classifiers for hyperspectral classification. This paper presents two methods to combine magnitude and shape features, one for each classifier. As the magnitude and shape features are complementary, combining both features can improve the classification accuracy. First, magnitude features are represented by the spectral radiance vector, whereas shape features are represented by the spectral gradient vector. Then, in SAM, each feature vector generates a spectral angle for each class. The two generated angles are added together to obtain a single similarity, which is used for the final classification. Similarly, in MLC, after the dimensionality reduction using Fisher's linear discriminant (FLD), each feature vector in the new feature space generates a likelihood. The two generated likelihoods are multiplied to obtain a single value, which is adopted for the final classification. Experimental results on an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data set demonstrate that the proposed methods outperform the methods with a single feature set.