Cross-Scale Mixing Attention for Multisource Remote Sensing Data Fusion and Classification

Abstract

Hyperspectral and multispectral images (HS/MS) fusion and classification as an important branch of data quality improvement and interpretation, has attracted increasing attention in recent years. However, the unavailable sensor prior still limits the performance of many traditional fusion methods, consequently deteriorating the classification results. Despite the unsupervised methods based on convolutional neural network (CNN) making a lot of attempts to mitigate the limitations, challenges with extracting the long-range dependencies hamper the performance. To address these impediments, a transformer-based baseline constructed by the cross-scale mixing attention (CSMFormer) is designed for HS/MS fusion and classification. Especially, the spatial-spectral mixer (SSMixer) is utilized to extract the long-range dependencies at large scale. Simultaneously, cross-scale feature calibration is achieved by combining information from the original scale. After that, nonlinear enhancement module (NLEM) is designed to encourage feature discrimination. Note that the spatial and spectral mixers can be replaced by any spatial-spectral feature extractors. Therefore, the proposed CSMFormer is flexible in data fusion, land-covers classification, segmentation, etc. Experiments about data fusion and land-covers classification on two HS/MS wetland remote sensing scenes demonstrate the superiority of the proposed CSMFormer baseline, improving the data quality and classification precision.