Hyperspectral image classification via nonlocal joint kernel sparse representation based on local covariance

Abstract

Although spectral-spatial information can provide significant improvement for the accuracy of hyperspectral image (HSI) classification, it remains a challenging task to achieve better classification results with a small amount of training samples. To tackle this problem, we develop a novel nonlocal joint kernel sparse representation method based on local covariance (NLJKSR-LC) for HSI classification in this paper. Firstly, the maximum noise fraction (MNF) is applied to the original HSI to reduce the computational complexity. Also, several most similar local neighbouring pixels for each sample are refined from their corresponding superpixel to explore the local spatial information accurately. Then, we calculate the local covariance descriptor (LCD) among each pixel and its local neighbours, which can explore the spectral correlation of different spectral bands and offer important discriminative information for classification. Next, several nonlocal similar samples are searched from the whole HSI based on the nonlocal self-similarity property and a novel NLJKSR-LC model is established to address the problem of classification. The NLJKSR-LC model takes the local spatial context and the global structure of HSI into full consideration at the same time, which can improve the classification accuracy especially when there is a small amount of training samples. In addition, we design an appropriate kernel to address the problem that LCD lies in the linearly inseparable manifold space. Experimental results on three real-world HSI datasets demonstrate that the proposed NLJKSR-LC method outperforms the state-of-the-art classification methods with a small amount of training samples.