Multi-resolution Pyramid Enhanced Non-local Feature Extraction for Hyperspectral Classification

Hyperspectral image (HSI) is applied to accurately distinguish ground objects in many fields, owing to its abundant structural and spectral information. To fully mine the spectral-spatial information, feature extraction methods are employed to further improve the classification performance in numerous research works. Among them, integrating contextual information into pixels is an active topic and achieved significant performance. However, it is inevitable to come with unwanted and superfluous distracting contents in the process of introducing contextual information. This article proposes a multi-resolution pyramid enhanced non-local feature extraction (MRPEN) method by exploiting non-local information from different resolutions to alleviate the effects of noise contents. The feature extraction task is implemented through by first constructing a multi-resolution feature pyramid via performing spectral-spatial reduction and resize operation on the HSI. With the feature pyramid, three kinds of feature extraction manners (i.e., short-range, long-range, and non-local) are utilized to extract specific features for each layers. In particular, the non-local feature is extracted by introducing adjacent superpixels from the upper layer into our feature extraction operation. After this, the extracted features of each layer are concatenated together and fed on a sparse autoencoders. Finally, a decision rule is introduced to blend the prediction results. Experiments on three public HSI datasets and a Gaofen-5 satellite dataset demonstrate that our method outperforms several state-of-the-art classification approaches.