Vegetation type recognition in hyperspectral images using a conjugacy indicator

This paper considers a vegetation type recognition algorithm in which the conjugacy indicator with a subspace spanned by endmember vectors is taken as a proximity measure. We show that with proper data preprocessing, including vector components weighting and class partitioning into subclasses, the proposed method offers a higher recognition quality when compared to a support vector machine (SVM) method implemented in MatLab software. This implementation provides good results with the SVM method for a fairly difficult classification test using the Indian Pines dataset with 16 classes containing similar vegetation types. The difficulty of the test is caused by high correlation between the classes. Thus, the results show a possibility for the recognition of a large variety of vegetation types, including the narcotic plants.