Multi-source remote sensing image classification based on Mamba has received increasing attention. However, existing methods do not consider the non-causal characteristics of visual data, which leads to insufficient extraction of global features by the model. To alleviate this problem, a novel network called TBi-Mamba is proposed for joint classification of hyperspectral and LiDAR data. Firstly, a cross-modal knowledge search module (CMKS) is designed, which effectively captures local features in different modalities through multi-scale feature extraction and interaction between multi-modal data. Secondly, a triple bidirectional sequence scanning mamba module (TBi-M) is proposed, which comprehensively considers multimodal information from the perspective of bidirectional sequence scanning, and introduces Mamba to accurately model global dependencies. Finally, a mixed feature reconstruction module (MFRM) is constructed. This module constructs an auxiliary loss function by reconstructing images of different modalities, providing more comprehensive supervision information and thus improving the performance of the model. The proposed method was evaluated on three publicly available datasets, and experimental results fully demonstrated that the classification performance of the proposed method is superior to that of some state-of-the-art (SOTA) methods.

The rapid development of sensor and multimodal technology has provided more possibilities for multisource remote sensing image classification. However, some existing joint classification methods are limited to single-level feature fusion and fail to fully explore the deep correlation between cross-level features, thus limiting the effective interaction and complementarity of information between different modal data. To alleviate this issue, this article proposes a hierarchical multimodal feature aggregation-based multihead axial attention transformer (HMAT) for joint classification of hyperspectral and light detection and ranging (LiDAR) data. First, a hierarchical multimodal feature aggregation module (HMFA) is proposed to more effectively fuse spatial–spectral features of hyperspectral images (HSIs) and elevation features of LiDAR data and generate more discriminative low-dimensional feature representations. Second, a pyramid-inverted pyramid convolution module (PIP) is designed. Through the complementary feature extraction structure, PIP can more fully capture the multiscale local features in the fused feature map of hyperspectral and LiDAR data. Finally, a multihead axial attention (MHAA) component is constructed to capture information at different scales in the fused feature maps, thereby accurately modeling global dependencies. The proposed HMAT has been extensively tested on three publicly available datasets. The experimental results demonstrate that the classification performance of the proposed method outperforms that of several state-of-the-art methods.

Remote sensing images obtained at high altitudes often contain complete object or scene information, which makes their global visual features richer compared to natural images. In order to enhance the scope and multilevel characteristics of global visual features of remote sensing images, this article proposes an enhanced global feature-guided network based on multiple filtering noise reduction (GFRNet) for remote sensing image compression. First, a pyramid vision transformer (PVT) is introduced into remote sensing image compression for the first time. Based on this, a PVT compression branch (PVTCB) is designed, which can capture multilevel global visual features through a three-stage pyramid transformer module for image compression (TPTC) and utilizes filters to accurately control the output of TPTC. Second, a quadruple-filtered multicore noise reduction attention module (QFMR-AM) is constructed in the four-stage compression branch (FSCB) for denoising and enhancing multilevel features. Finally, a global visual feature guidance module (GVGM) is designed between FSCB and the four-stage reconstruction decoder (FSRD). By calculating the global visual feature loss LossGVF through GVGM, a novel rate-distortion LossTotal is constructed, making the network more focused on extracting global information. Experimental results show that compared with some advanced methods, the proposed GFRNet achieves better compression performance on multiple evaluation indicators. In addition, the reconstructed images obtained by the proposed GFRNet can provide better classification performance, which further proves that the proposed method helps to preserve more important features of remote sensing images during the compression process.

Remote sensing image compression networks aim to enhance the similarity between the input image and the reconstructed image. The current network rarely considers the potential relationship between the compression features of different levels and the reconstruction features of the corresponding levels, which limits the improvement of remote sensing image compression performance. In this article, a concept of multilevel domain similarity is first proposed, which fully develops the multilevel domain similarity between the encoding and decoding processes to improve the quality of reconstructed images. On this basis, a multilevel domain similarity enhancement guided network (MDSNet) is proposed for remote sensing image compression. First, an efficient compression baseline network (BaselineA) was proposed, which realizes efficient image compression with low computational complexity. Second, a multilevel domain similarity enhancement module (MDEM) was designed, which improved the quality of the reconstructed image by enhancing the multilevel domain similarity. Third, a global information-enhanced attention module (GIE-AM) was constructed to enhance channel features and global features. Finally, under the guidance of the total loss (LossTotal), which is constructed by the proposed MDEM loss (MDEM-Loss), an effective compression was implemented by the whole network for remote sensing image compression. Experimental results show that compared with some advanced compression models, the proposed MDSNet can significantly improve compression performance with lower computational complexity. In addition, the reconstructed images obtained by the proposed method can provide better classification performance, which further proves that the proposed MDSNet helps to preserve more important features of remote sensing images during the compression process.

For hyperspectral image classification (HSIC), labeling samples is challenging and expensive due to high dimensionality and massive data, which limits the accuracy and stability of classification. To alleviate this problem, a greedy strategy guided graph self-attention network (GS-GraphSAT) is proposed. First, a graph self-attention (GSA) mechanism is designed by combining a multihead self-attention (MHSA) mechanism with the graph attention network (GAT), which can simultaneously consider the direct and indirect relationships between nodes and deeply analyze the intrinsic characteristics of nodes. Second, a multiattention fusion (MAF) module is developed, which utilizes multiscale convolution kernels and attention mechanisms to significantly enhance the network’s ability to extract local features from images at the pixel level, thereby further enriching the hierarchy and diversity of features. Finally, a greedy training strategy (GTS) is proposed. During the training process, GTS accurately determines the optimal time to supplement samples by analyzing the changes in losses, thereby achieving a significant improvement in network classification performance with limited samples. Extensive experiments were conducted on four challenging datasets. The results demonstrate that the proposed method significantly outperforms other state-of-the-art methods in terms of classification accuracy and robustness. The performance improvement of overall accuracy (OA) can reach up to 1.70% in Houston 2013 (HT). The codes of this work will be available athttps://github.com/Isee-max/IEEE_TGRS_GS-GraphSATfor reproduction.