Hyperspectral Image Classification via Superpixel Spectral Metrics Representation

This letter proposes a new hyperspectral classification method that fuses superpixel spectral metrics and joint sparse representation (JSR), which is termed as superpixel spectral metrics representation (SSMR). Recently, superpixel segmentation has proven to be a powerful tool to exploit the spatial information of hyperspectral images (HSIs), since the size and shape of each superpixel can be adaptively changed in different structural textures. Moreover, spectral information divergence (SID) has superiority compared to other distance-based similarity measures, particularly when using with a JSR classifier. Taking the aforemementioned advantages into account, superpixel segmentation, SID, and JSR are availably combined to effectively utilize the spectral-spatial information of the HSI. The proposed SSMR method includes the following main steps. First, superpixel segmentation is utilized to divide the original map into several superpixels. Second, similarity metric SID among test samples in all superpixels and training samples are calculated. Next, the JSR model is employed to obtain the reconstruction residuals of each class. Then, a regularization parameter $\lambda$ is introduced to attain balance between JSR and SID. Finally, pixel's label is determined by the minimal total residual. Experimental results on the Indian Pines dataset show better performance than several well-known classification methods.