From Coarse to Fine: Learning Semantic Relations for Hyperspectral Image Classification

Hyperspectral images (HSIs) is consisted of many narrow spectral bands which are capable of recording abundant features including both the spectral and spatial signatures information which have been widely used in various fields, such as urban planning, disaster monitoring. Due to the large number and high similarity of the collected spectral brands, many methods are developed to handle the problem of extracting effective features. Recently, many CNN-based methods have been proposed by exploiting the spectral-spatial signatures of the HSIs data and achieved promising results. Although many methods adopt patch-based input pattern to emphasize the importance of the spatial neighbor information of each pixel, the relations are still limited to a small area around the pixel and the latent relations among the pixels belonging to different semantic categories at the boundary are still not well exploited. To explore the relationships between pixels from a more global perspective, a neighbor-based relation mining framework is proposed to explore the long-range relations among different local regions. Experiments are conducted on two hyperspectral image classification datasets and the results demonstrate the effectiveness of the proposed long-range relations mining scheme by comparison with some state-of-the-art methods.