Deep learning models have shown excellent performance in the hyperspectral remote sensing image (HSI) classification. In particular, convolutional neural networks (CNNs) have received widespread attention because of their powerful feature-extraction ability. Recently, a capsule network (CapsNet) was introduced to boost the performance of CNNs, marking a remarkable progress in the field of HSI classification. In this article, we propose a novel deep convolutional capsule neural network (DC-CapsNet) based on spectral-spatial features to improve the performance of CapsNet in the HSI classification while significantly reducing the computation cost of the model. Specifically, a convolutional capsule layer based on the extension of dynamic routing using 3-D convolution is used to reduce the number of parameters and enhance the robustness of the learned spectral-spatial features. Furthermore, a lighter and stronger decoder network composed of deconvolutional layers as a better regularization term and capable of acquiring more spatial relationships is used to further improve the HSI classification accuracy with low computation cost. In this study, we tested the performance of the proposed model on four widely used HSI datasets: the Kennedy Space Center, Indian Pines, Pavia University, and Salinas datasets. We found that the DC-CapsNet achieved high classification accuracy with limited training samples and effectively reduced the computation cost.

Hyperspectral remote sensing images (HSIs) are rich in spectral-spatial information. The deep learning models can help to automatically extract and discover these rich information from HSIs for classifying HSIs. However, the sampling of the models and the design of the hyperparameters depend on the number of samples and the size of each sample's input space. In the case of limited samples, the description dimension of features is also limited and overfitting to other remote sensing image datasets is evident. This study proposes a novel deep feature aggregation network (DFAN) for HSI classification based on a 3D-convolutional neural network (3D-CNN) from the perspective of feature aggregation patterns. By introducing the residual learning and dense connectivity strategies, we established a deep feature residual network (DFRN) and a deep feature dense network (DFDN) to exploit the low-, middle- and high-level features in HSIs. For the Indian Pines and Kennedy Space Center datasets, the DFRN model was determined to be more accurate. On the Pavia University dataset, both the DFDN and DFRN have basically the same accuracy, but the DFDN has faster convergence speed and more stable performance on the validation set than the DFRN. Therefore, when faced with different HSI data, the corresponding aggregation method can be chosen more flexibly according to the requirements on number of training samples and the convergence speed. This is beneficial in HSI classification.

Models based on capsule neural network (CapsNet), a novel deep learning method, have recently made great achievements in hyperspectral remote sensing image (HSI) classification due to their excellent ability to implicitly model the spatial relationship knowledge embedded in HSIs. However, the number of labeled samples is a common bottleneck in HSI classification, limiting the performance of these deep learning models. To alleviate the problem of limited labeled samples and further explore the potential of CapsNet in the HSI classification field, this study proposes a multiscale feature aggregation capsule neural network (MS-CapsNet) based on CapsNet via the implementation of two branches that simultaneously extract spectral, local spatial, and global spatial features to integrate multiscale features and improve model robustness. Furthermore, because deep features are generally more discriminative than shallow features, two kinds of capsule residual (CapsRES) blocks based on 3D convolutional capsule (3D-ConvCaps) layers and residual connections are proposed to increase the depth of the network and solve the limited labeled sample problem in HSI classification. Moreover, a squeeze-and-excitation (SE) block is introduced in the shallow layers of MS-CapsNet to enhance its feature extraction ability. In addition, a reasonable initialization strategy that transfers parameters from two well-designed, pretrained deep convolutional capsule networks is introduced to help the model find a good set of initializing weight parameters and further improve the HSI classification accuracy of MS-CapsNet. Experimental results on four widely used HSI datasets demonstrate that the proposed method can provide results comparable to those of state-of-the-art methods.

Recent advances in deep learning have significantly improved hyperspectral image (HSI) classification. However, deep learning models for HSI classification typically rely on one-hot labels, which lack semantic information and fail to reflect relationships between land cover classes, leading to suboptimal generalization and interpretability. To overcome these problems, we propose a novel knowledge graph (KG)-guided HSI classification framework that bridges symbolic reasoning and connectionist learning. Unlike previous methods, our framework incorporates a KG to encode explicit symbolic knowledge, providing a structured definition of land cover classes. We employ KG embedding (KGE) techniques to transform symbolic knowledge into continuous vector representations, seamlessly integrating structured semantics with deep learning models. Furthermore, we develop two KG integration approaches: a regression-based method (KGR) and a classification-based method (KGC), demonstrating the complementary role of structured symbolic knowledge in enhancing connectionist learning. Extensive experiments on three representative HSI datasets show that integrating KG significantly improves the performance of a wide range of deep learning architectures in HSI classification, highlighting the broad applicability and robustness of the proposed framework across diverse deep learning architectures.