Kai Jiang

Recently, hyperspectral target detection (HTD) technique based on deep learning (DL) has been developed rapidly. However, existing algorithms show poor generalization across different hyperspectral images (HSIs), where repeated training and inference based on limited prior information are necessary. To liberate HTD from dependence on the quantity and the quality of training samples, this article proposes a signature-constrained two-stage framework for HTD (HTD-STF) based on generative self-supervised learning (GSSL). In the first stage for pre-training, to realize spectral-spatial reconstruction, we build an asymmetric autoencoder (AE) employing transformer blocks with long-range perception for generalized feature extraction. During pre-training, the spectral-spatial similarity loss is designed to improve the effect of reconstruction. In the second stage for fine-tuning and detection, the signature is utilized in preprocessing, training and inference, respectively. Specifically, the coarse sample mining and tiling strategy in preprocessing not only facilitates the framework in flexible input dimension, but also provides pseudo labels for end-to-end training. During training, we adopt the signature as guidance for feature-level fusion, which alleviates the impact of sample imbalance. After training, the final inference based on pixel-level fusion refines the original output. For ideal GSSL, the HyperMix-10K, a new large-scale hyperspectral dataset, has been constructed in this work, which contains numerous unlabeled HSIs captured in various scenes. Experimental results and analysis on real HSIs verify the effectiveness and generalization ability of the HTD-STF.

Cross-view geo-localization provides an offline visual positioning strategy for unmanned aerial vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments. However, it still faces the following challenges, leading to suboptimal localization performance: 1) Existing methods primarily focus on extracting global features or local features by partitioning feature maps, neglecting the exploration of spatial information, which is essential for extracting consistent feature representations and aligning images of identical targets across different views. 2) Cross-view geo-localization encounters the challenge of data imbalance between UAV and satellite images. To address these challenges, the Spatial Hybrid Attention Network with Adaptive Cross-Entropy Loss Function (SHAA) is proposed. To tackle the first issue, the Spatial Hybrid Attention (SHA) method employs a Spatial Shift-MLP (SSM) to focus on the spatial geometric correspondences in feature maps across different views, extracting both global features and fine-grained features. Additionally, the SHA method utilizes a Hybrid Attention (HA) mechanism to enhance feature extraction diversity and robustness by capturing interactions between spatial and channel dimensions, thereby extracting consistent cross-view features and aligning images. For the second challenge, the Adaptive Cross-Entropy (ACE) loss function incorporates adaptive weights to emphasize hard samples, alleviating data imbalance issues and improving training effectiveness. Extensive experiments on widely recognized benchmarks, including University-1652, SUES-200, and DenseUAV, demonstrate that SHAA achieves state-of-the-art performance, outperforming existing methods by over 3.92%. Code will be released at: https://github.com/chennanhua001/SHAA.

Multi-modal fusion holds great promise for integrating information from different modalities. However, due to a lack of consideration for modal consistency, existing multi-modal fusion methods in the field of remote sensing still face challenges of incomplete semantic information and low computational efficiency in their fusion designs. Inspired by the observation that the visual language pre-training model CLIP can effectively extract strong semantic information from visual features, we propose M³amba, a novel end-to-end CLIP-driven Mamba model for multi-modal fusion to address these challenges. Specifically, we introduce CLIP-driven modality-specific adapters in the fusion architecture to avoid the bias of understanding specific domains caused by direct inference, making the original CLIP encoder modality-specific perception. This unified framework enables minimal training to achieve a comprehensive semantic understanding of different modalities, thereby guiding cross-modal feature fusion. To further enhance the consistent association between modality mappings, a multi-modal Mamba fusion architecture with linear complexity and a cross-attention module Cross-SS2D are designed, which fully considers effective and efficient information interaction to achieve complete fusion. Extensive experiments have shown that M³amba has an average performance improvement of at least 5.98% compared with the state-of-the-art methods in multi-modal hyperspectral image classification tasks in the remote sensing field, while also demonstrating excellent training efficiency, achieving a double improvement in accuracy and efficiency. The code is released at https://github.com/kaka-Cao/M3amba.

Recently, generative self-supervised learning (GSSL) has gained extensive attention in hyperspectral remote sensing. For the hyperspectral target detection (HTD) task, traditional GSSL-based algorithms usually require hyperspectral images (HSIs) as additional datasets for pre-training, which are relatively resource-intensive and time-consuming. To better interpret the spectral-spatial information of HSIs while alleviating the dependence on large-scale hyperspectral datasets, we develop a novel two-stage framework for HTD based on GSSL in this article. In the preprocessing for the input hyperspectral image (HSI), a dimensional transformation (DT) module and a coarse detection reference (CDR) module are constructed to produce feature patches as training samples for subsequent pre-training and fine-tuning. In the pre-training stage for spectral-spatial reconstruction, we construct an asymmetric autoencoder (AE) architecture which leverages the transformer blocks with long-range perception to extract generalized features and explore discriminative feature representations of the input HSI. Specifically, a dual-stream wavelet patch embedding (DWPE) module is proposed to integrate the wavelet transform (WT) mechanism with the convolutional neural networks (CNNs), which extracts robust spectral-spatial features by performing convolution operations with different frequency components of WT. In the fine-tuning stage, a novel signature-constrained cross-entropy (SC-CE) loss function is proposed to constrain the network optimization. For the final detection, a pixel-level fusion based on coarse detection (CDPF) module is employed after inference to further suppress the interference from background. Experimental results on six real HSIs demonstrate that the proposed method achieves superior detection performance while maintaining the generalization of the pre-trained model.

Multi-modal fusion holds great promise for integrating information from different modalities. However, due to a lack of consideration for modal consistency, existing multi-modal fusion methods in the field of remote sensing still face challenges of incomplete semantic information and low computational efficiency in their fusion designs. Inspired by the observation that the visual language pre-training model CLIP can effectively extract strong semantic information from visual features, we propose M$^3$amba, a novel end-to-end CLIP-driven Mamba model for multi-modal fusion to address these challenges. Specifically, we introduce CLIP-driven modality-specific adapters in the fusion architecture to avoid the bias of understanding specific domains caused by direct inference, making the original CLIP encoder modality-specific perception. This unified framework enables minimal training to achieve a comprehensive semantic understanding of different modalities, thereby guiding cross-modal feature fusion. To further enhance the consistent association between modality mappings, a multi-modal Mamba fusion architecture with linear complexity and a cross-attention module Cross-SS2D are designed, which fully considers effective and efficient information interaction to achieve complete fusion. Extensive experiments have shown that M$^3$amba has an average performance improvement of at least 5.98\% compared with the state-of-the-art methods in multi-modal hyperspectral image classification tasks in the remote sensing field, while also demonstrating excellent training efficiency, achieving a double improvement in accuracy and efficiency. The code is released at https://github.com/kaka-Cao/M3amba.

Accurate cloud recognition and warning are crucial for various applications, including in-flight support, weather forecasting, and climate research. However, recent deep learning algorithms have predominantly focused on detecting cloud regions in satellite imagery, with insufficient attention to the specificity required for accurate cloud recognition. This limitation inspired us to develop the novel FY-4A-Himawari-8 (FYH) dataset, which includes nine distinct cloud categories and uses precise domain adaptation methods to align 70,419 image-label pairs (including 110,000Train/5,500Test 100 × 100 size images) in terms of projection, temporal resolution, and spatial resolution, thereby facilitating the training of supervised deep learning networks. Given the complexity and diversity of cloud formations, we have thoroughly analyzed the challenges inherent to cloud recognition tasks, examining the intricate characteristics and distribution of the data. To effectively address these challenges, we designed a Distribution-aware Interactive-Attention Network (DIAnet), which preserves pixel-level details through a high-resolution branch and a parallel multi-resolution cross-branch. We also integrated a distribution-aware loss (DAL) to mitigate the imbalance across cloud categories. An Interactive Attention Module (IAM) further enhances the robustness of feature extraction combined with spatial and channel information. Empirical evaluations on the FYH dataset demonstrate that our method outperforms other cloud recognition networks, achieving superior performance in terms of mean Intersection over Union (mIoU). The code for implementing DIAnet is available at https://github.com/icey-zhang/DIAnet.

Despite the great potential of convolutional neural networks (CNNs) in various tasks, the resource-hungry nature greatly hinders their wide deployment in cost-sensitive and low-powered scenarios, especially applications in remote sensing. Existing model pruning approaches, implemented by a “subtraction” operation, impose a performance ceiling on the slimmed model. Self-knowledge distillation (Self-KD) resorts to auxiliary networks that are only active in the training phase for performance improvement. However, the knowledge is holistic and crude, and the learning-based knowledge transfer is mediate and lossy. Here, we propose a novel model-compression method, termed block-wise partner learning (BPL), which comprises “extension” and “fusion” operations and liberates the compressed model from the bondage of baseline. Different from the Self-KD, the proposed BPL creates a partner for each block for performance enhancement in training. For the model to absorb more diverse information, a diversity loss (DL) is designed to evaluate the difference between the original block and the partner. Besides, the partner is fused equivalently instead of being discarded directly. After training, we can simply adopt the fused compressed model that contains the enhancement information of partners but with fewer parameters and less inference cost. As validated using the UC Merced land-use, NWPU-RESISC45, and RSD46-WHU datasets, the BPL demonstrates superiority over other compared model-compression approaches. For example, it attains a substantial floating-point operations (FLOPs) reduction of 73.97% with only 0.24 accuracy (ACC.) loss for ResNet-50 on the UC Merced land-use dataset. The code is available at https://github.com/zhangxin-xd/BPL.