Hyperspectral Image Classification via Exploring Spectral–Spatial Information of Saliency Profiles

Morphological features have shown promising performances for hyperspectral image (HSIs) classification, as they can efficiently extract the multilevel spatial information of HSIs. However, the objects in the scenes are always with different sizes and shapes, making it difficult to excavate spatial information of important structures completely by the existing morphological methods. To address this problem, we propose a novel two-stage framework based on morphology and superpixel. Specifically, we propose self-dual saliency profiles (SPs) based on a saliency measure considering the grayscale contrast within objects and edge information. SPs are hierarchical features that characterize spatial information for salient objects whose saliency index is the significant local maxima. The SPs are constructed based on a two-step algorithm. First, all salient objects of different shapes in the scene are preserved, and the undesired spatial details are discarded by attribute filters based on the saliency measure. Second, the morphological feature is generated based on the organization structure of salient objects in the scene, which provides hierarchical spatial features of the image. Then, the superpixel segmentation is performed on each of the extracted SPs on the basis of the spatial information of salient objects. And, two types of superpixel-based features are extracted from SPs to exploit the information in SPs. The extracted innersuperpixel and intersuperpixel features are fused with spectral information to produce the classification map. The experiments conducted on three HSIs show that, the proposed approach significantly outperforms the other state-of-the-art methods.