中国科学院自动化研究所机构知识库

  Classification of hyperspectral images is an important issue in remote sensing image processing systems. Hyperspectral images have advantages in pixel-wise classification owing to the high spectral resolution. However, the pixel-wise classification result often introduces the salt-and-pepper appearance because of the complex noise produced by atmosphere and instrument. An effective way to overcome this phenomenon is to resort to

the spatial information. This paper proposes a method to solve the above problem by using spatial similarity information. First, in order to avoid the effect of noisy pixels and mixed pixels, reliable seeds are selected in local windows according to the agreement between the central pixel and its spatial neighbors. Then, the information of the reliable seeds is propagated to their spatial neighbors by a graph Laplacian. Specifically, the graph Laplacian is designed to propagate information among spatial neighbors with close similarity relationship so that some small or long thin objects are identified. Through the seed selection and local reliable information propagation, the problem of noisy labels is solved elegantly. Experiments on three real hyperspectral data sets with different spatial resolution, spectral resolution and land covers demonstrate the effectiveness of our method.