Classification of hyperspectral image by preprocessing method based relation network

Abstract

Hyperspectral image classification involves assigning a land cover class to each pixel of a hyperspectral image (HSI). Recently, several classification methods based on deep learning with spatial information from HSI have been proposed. These methods typically demonstrate strong classification capabilities. However, spatial information often comprises complex data, making it challenging to extract valuable information. To address this issue, this paper proposes a novel image preprocessing method for classification based on metric learning. This method aims to reduce the complexity of spatial information to facilitate network learning. First, the method assesses the similarity between the central pixel and other pixels in each image patch using a relation network. It replaces pixels that are dissimilar to the central pixel with pixels that are similar to the central pixel to simplify the image patch. This approach reduces interference information in the image patch, allowing the network to focus more on the central pixel features that represent category information during the classification task. This greatly reduces the complexity of the required network, the difficulty of the network learning, and the number of training samples needed. Experimental results on three popular hyperspectral image datasets demonstrate that the proposed preprocessing method significantly improves classification accuracy compared to the baseline network. The study also compares our method to the state-of-the-art attention mechanism CNN, demonstrating how our method excels at hyperspectral classification in data-smoothing and feature preservation.