A SUPERPIXEL-BASED FRAMEWORK FOR NOISY HYPERSPECTRAL IMAGE CLASSIFICATION

Random noise in hyperspectral images (HSIs) may significantly degrade the image quality and further affect the subsequent image applications, such as land cover classification. To improve the performance of the existing classification methods on noisy HSIs, we propose a framework to take full advantages of the superpixel segmentation and the traditional pixel-wise classification methods. First, a novel superpixel model is proposed for HSI segmentation, where a new spectral similarity is defined in wavelet domain to make the superpixel model more robust to random noise; then, a simple but effective fusion strategy is designed to combine the superpixels with the pixel-wise classification results. Experimental results demonstrate the effectiveness of the proposed superpixel model and fusion strategy on noisy HSIs.