International Journal of Applied Earth Observations and Geoinformation (Mar 2024)
Two-Stream spectral-spatial convolutional capsule network for Hyperspectral image classification
Abstract
Recently, the capsule network and its enhanced version named convolutional capsule network (Conv-CapsNet) were applied to hyperspectral image (HSI) classification and delivered outstanding performance, attributing to the enriched capabilities of capsules for feature expression and complex spatial knowledge excavation. However, restricted by the locality of convolutions in primary feature extraction, the rich spectral-spatial diagnostic information found in HSIs were insufficiently utilized, which degrades classification performance to a certain degree. In addition, most capsule networks involve a complex structure with many parameters, which are haunted by a common bottleneck of limited available training samples. To tackle these challenges, this paper presents a novel two-stream spectral-spatial convolutional capsule network (TSCCN) for HSIs to boost accurate classification with small samples. Based on the advanced convolutional capsule concept, a two-stream Conv-CapsNet architecture is developed that reduces trainable parameters and mitigates overfitting when samples are insufficient. Specifically, a 1D Conv-CapsNet and a 2D Conv-CapsNet sharing similar structures are developed as accompanying streams to fully excavate favorable features from the continuous spectra and raw spectral-spatial cubes, with the separate features fused to generate joint deep features for classification. To overcome the locality of primary convolutions, an efficient structural information mining module (SIM) is designed for each stream, to learn complementary cross-channel and cross-kernel inter-dependencies for subsequent capsule learning and further improve classification performance. Experiments on three well-known HSI datasets demonstrate the superiority of the proposed algorithm over nine representative state-of-the-art deep classifiers and its efficacy under small sample conditions, where all the methods were implemented in the Tensor-Flow framework and repeated for 10 times.
