Classification plays a significant role in analyzing remotely sensed imagery. In order to obtain an optimized classier, following aspects are rather challenging: 1) complexity in dealing with the overwhelming amount of data information from an advanced high resolution hyperspectral imaging sensor; 2) difficulty in leveraging spectral and spatial information across the sensed wavelengths; 3) struggles in obtaining adequate dataset as in the same modalities with labeled ground truth in the training process. Therefore, we propose a novel classification approach to tackle these issues by utilizing probabilistic graphical model on super-pixel segmentation. This method is capable of compacting hyperspectral information efficiently which decreases computing complexity. Moreover, the employment of probabilistic graphical models that weighs the strong dependency in spatial and spectral neighbors improves accuracy. One of the most successful probabilistic graphical models is Conditional Random Fields (CRFs). Conventional methods utilize all spectral bands and assign the corresponding raw intensity values into the feature functions in CRFs and build the grid graph. These methods, however, require significant computational efforts and yield an ambiguous summary from the data. To mitigate these problems, we cooperate a non-linear kernel based classier to provide the meaningful probability features for CRFs and learn the non-grid graph from super pixel segmentation.
['Yang Hu', 'Sildomar T. Monteiro', 'Eli Saber']
Super pixel based classification using conditional random fields for hyperspectral images