Joint superpixel and Transformer for high resolution remote sensing image classification

Abstract

Deep neural networks combined with superpixel segmentation have proven to be superior to high-resolution remote sensingimage (HRI) classification. Currently, most HRI classification methods that combine deep learning and superpixel segmentationuse stacking on multiple scales to extract contextual information from segmented objects. However, this approach does nottake into account the contextual dependencies between each segmented object. To solve this problem, a joint superpixel andTransformer (JST) framework is proposed for HRI classification. In JST, HRI is first segmented into superpixel objects as input,and Transformer is used to model the long-range dependencies. The contextual relationship between each input superpixelobject is obtained and the class of analyzed objects is output by designing an encoding and decoding Transformer. Additionally,we explore the effect of semantic range on classification accuracy. JST is also tested by using two HRI datasets with overallclassification accuracy, average accuracy and Kappa coefficients of 0.79, 0.70, 0.78 and 0.91, 0.85, 0.89, respectively. Theeffectiveness of the proposed method is compared qualitatively and quantitatively, and the results achieve competitive andconsistently better than the benchmark comparison method.