The abundant spectral signatures and spatial contexts are effectively utilized as the key to hyperspectral image (HSI) classification. Existing convolutional neural networks (CNNs), only focus on locally spatial context information and lack the ability to learn global spectral sequence representations, whereas the transformer performs well in learning the global dependence of sequential data. To solve this issue, inspired by the transformer, we propose an interactive global spectral and local spatial feature fusion transformer called ISSFormer. Specifically, we achieve an elegant integration of self-attention and convolution in a parallel design, i.e., the multi-head self-attention mechanism (MHSA) and the local spatial perception mechanism (LSP). ISSFormer can learn both local spatial feature representation and global spectral feature representation simultaneously. More significantly, we propose a bi-directional interaction mechanism (BIM) of features across the parallel branch to provide complementary clues. The local spatial features and the global spectral features interact through the BIM which could emphasize the local spatial details and add spatial constraints to overcome spectral variability, and can further improve classification performance. With extensive experiments on three benchmark datasets, including Indian Pines, Pavia University, and WHU-Hi-HanChuan, ISSFormer can accomplish superior classification accuracy and visualization performance.

Recently, multi-modal remote sensing image (MRSI) classification has attracted increasing attention of researchers. However, classification of MRSI with limited labeled instances is still a challenging task. In this paper, a novel self-supervised cross-modal contrastive learning method is proposed for MRSI classification. Joint intra- and cross-modal contrastive learning are used to better mine multi-modal feature representations during pre-training, and the intra- and cross-modal contrastive learning objectives are jointly optimized, whereby it encourages the learned representation to be semantically consistent within and between modalities simultaneously. Moreover, a simple but effective hybrid cross-modal fusion module (HCFM) is designed in the fine-tuning stage, which could better compactly integrate complementary information across these modalities for more accurate classification. Extensive experiments are taken on four benchmark datasets (i.e., Houston 2013, Augsburg, Trento, and Berlin), and the results show that the proposed method outperforms state-of-the-art methods.

Due to the scarcity and high cost of labeled hyperspectral image (HSI) samples, many deep learning methods driven by massive data cannot achieve the intended expectations. Semi-supervised and self-supervised algorithms have advantages in coping with this phenomenon. This paper primarily concentrates on applying self-supervised strategies to make strides in semi-supervised HSI classification. Notably, we design an effective and a unified self-supervised assisted semi-supervised residual network (SSRNet) framework for HSI classification. The SSRNet contains two branches, i.e., a semi-supervised and a self-supervised branch. The semi-supervised branch improves performance by introducing HSI data perturbation via a spectral feature shift. The self-supervised branch characterizes two auxiliary tasks, including masked bands reconstruction and spectral order forecast, to memorize the discriminative features of HSI. SSRNet can better explore unlabeled HSI samples and improve classification performance. Extensive experiments on four benchmarks datasets, including Indian Pines, Pavia University, Salinas, and Houston2013, yield an average overall classification accuracy of 81.65%, 89.38%, 93.47% and 83.93%, which sufficiently demonstrate that SSRNet can exceed expectations compared to state-of-the-art methods.

Recently, Hyperspectral Image Classification (HIC) with noisy labels is attracting increasing interest. However, existing methods usually neglect to explore feature-dependent knowledge to reduce label noise, and thus perform poorly when the noise ratio is high or the clean samples are limited. In this paper, a novel Triple Contrastive Representation Learning (TCRL) framework is proposed from a deep clustering perspective for robust HIC with noisy labels. The TCRL explores the cluster-level, instance-level, and structure-level representation of HIC by defining triple learning loss. First, the strong and weak transformation are defined for hyperspectral data augmentation. Then, a simple yet effective lightweight Spectral Prior Attention-based Network (SPAN) is presented for spatial-spectral feature extraction of all augmented samples. Additionally, cluster-level and instance-level contrastive learning are performed on two projection subspaces for clustering and distinguishing samples respectively. Meanwhile, structure-level representation learning is employed to maximize the consistency of data after different projections. Taking the feature-dependent information learned by triple representation learning, our proposed end-to-end TCRL can effectively alleviate the overfitting of classifier to noisy labels. Extensive experiments have been taken on three public datasets with various noise ratios and two types of noise. The results show that the proposed TCRL could provide more robust classification results when training on noisy datasets compared with state-of-the-art methods, especially when clean samples are limited. The code will be available at https://github.com/Zhangxy1999.

In recent years, the joint classification approach of hyperspectral image (HSI) and light detection and ranging (LiDAR) data based on deep learning (DL) has received increasing attention. However, existing methods either lack interaction between heterogeneous features during feature extraction or treat them equally during the interaction, inevitably resulting in redundant information stacking and reaching the performance bottleneck. To this end, we propose a novel discrepant bi-directional interaction fusion network (DBIFNet) for the collaborative classification of HSI and LiDAR data. First, a discrepant bi-directional interaction module (DBDIM) is designed to establish correlations between heterogeneous features to enhance the respective feature learning. Furthermore, a cross-modal attention fusion module (CAFM) is developed to dynamically fuse multi-modal features, which can further improve classification performance. Extensive experiments on the Houston and Trento datasets demonstrate that the proposed DBIFNet can achieve competitive classification performance.

Exploring semantic structure information contained in the hyperspectral image (HSI) is significant for accurate HSI classification (HSIC), especially when there are very few labeled samples. Besides, it is usually time-consuming to process HSI for many algorithms. In this paper, for a more accurate and efficient HSIC, a Semantic CorrEntropy Representation-based method (SCER) is presented. Specifically, superpixel segmentation is employed to capture structural information and improve computational efficiency. Each superpixel in HSI can be regarded as a semantic subregion. And for each semantic subregion, we define a semantic correntropy matrix to represent the intrinsic spectral similarity distribution, which can provide beneficial information for classification. However, the spatial sizes of diverse land covers in HSI have large variations, which means the rich semantic information cannot be fully captured by a single-scale segmentation map. Therefore, multiscale SCER (MSCER) with the multiscale segmentation strategy is proposed. Further, the features learned from multiple scales are integrated via multiple kernels to promote semantic information learning. The weights of multiple kernels can be determined adaptively. The semantic features of diverse land covers represented by MSCER have shown great intra-class compactness and inter-class separability. Compared with other state-of-the-art methods, our proposed MSCER has achieved competitive classification performance both in accuracy and efficiency with few labeled samples.