SFE-FN: A Shuffle Feature Enhancement-Based Fusion Network for Hyperspectral and LiDAR Classification

Under the background of the rapid development of remote sensing (RS) technology, multimodal RS image classification has attracted great attention. Considerable research has been devoted to designing more adequate multimodal feature-level fusion networks. However, few have noted that in the process of feature fusion, if the multimodal heterogeneous features are quite different, direct fusion may introduce noise. This greatly affects the classification performance of the network. This letter proposes a shuffle feature enhancement-based fusion network (SFE-FN) for hyperspectral and light detection and ranging (LiDAR) classification, which effectively alleviates the aforementioned problems. Specifically, first, an SFE module is proposed to achieve self-enhancement and mutual enhancement of each modal feature to preliminary reduce the feature difference. Then, a cross-layer and cross-interaction module (CLCI) is designed to further enhance the consistency of features by updating parameters across layers. Finally, the proposed shuffle feature concatenation (SFC) module and the shuffle feature fusion (SFF) module are utilized to adequately merge fewer differentiated features. Experiments on Houston2013 and Trento datasets show that the proposed method is effective.