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Graph-based Adaptive Network With Spatial-Spectral Features for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12865-12881页

作者： Dong, Hua Zhang, Xiaohua Zhang, Jinhua Meng, Hongyun Jiao, Licheng Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710126 China Xidian University School of Mathematics and Statistics Xi'an710126 China

Hyperspectral unmixing aims to extract basic material (endmember) spectra and estimate their corresponding fractions (abundances) from observed pixels in hyperspectral images (HSIs). Recently, blind unmixing methods based on Autoencoders (AEs) have gained significant attention due to their capability to simultaneously obtain endmembers and abundances, as well as their strong performance. Also, many AE-based unmixing methods have explored the use of spatial information in HSIs to enhance unmixing performance. However, most of these methods fail to effectively integrate both local and global spatial-spectral features and lack adaptive selection and constraints for the extracted features. To address this issue, this paper proposes an adaptive spatial-spectral unmixing method based on Graph. In the method, HSIs are treated as data on manifold structures, with superpixels serving as graph nodes to construct a global graph-structured data. We then use Graph Attention Network (GAT) to learn superpixel-level global spatial-spectral feature representations from this graph-structured data. Since the spatial-spectral feature learned by GAT is at the superpixel level, it is impossible to unmix each pixel using only this feature. Thus, we integrate a Convolutional Neural Network (CNN) to learn local discriminative spatial-spectral features. Attention mechanisms are further employed to select the features most relevant to unmixing. Additionally, An Adaptive Abundance Constraint Module is proposed to dynamically enforce constraints on the extracted features. Experiments conducted on synthetic and real hyperspectral images demonstrate the efficiency and superior performance of our proposed method compared to several existing unmixing approaches. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Cascaded Sliding-Window-Based Relativistic GAN Fusion for Perceptual and Consistent Video Super-Resolution 6th

Cascaded Sliding-Window-Based Relativistic GAN Fusion for P...

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6th IFIP TC 12 International Conference on Intelligence Science, ICIS 2024

作者： Li, Dingyi PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783031712524

Perceptual video super-resolution aims at converting low-resolution videos to visually appealing high-resolution ones. It may lead to temporal inconsistency due to the drastically changing outputs. In this paper, we propose cascaded sliding-window-based relativistic GAN (Generative Adversarial Network) fusion for perceptual and consistent video super-resolution (PC-VSR). Firstly, cascaded sliding-window-based relativistic GAN is designed to extract more useful information. It enlarges the temporal receptive field of sliding-window-based model in each step. It is able to enhance perceptual quality and compensate temporal consistency progressively and sufficiently. The trained separate refinement generator networks are fused into a final refinement generator. The final refinement generator can be calculated recursively at the testing stage. With our generator fusion, the parameter number is reduced and good quality is maintained. Extensive experimental results demonstrate that our approach outperforms state-of-the-art super-resolution methods in terms of perceptual quality. Our method also achieves good temporal consistency and per-pixel accuracy, compared with other perceptual approaches. © IFIP International Federation for Information Processing 2025.

关键词： Generative adversarial networks

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DBA-RMCL: Refined Metric Contrastive Learning With Dual-Branch Attention for Few-Shot Remote Sensing image Scene Classification

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Yang, Shuting Chen, Hao He, Fachuan Chen, Wen Chen, Ting He, Jianjun Jinling Institute of Technology Nanjing China Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education School of Artificial Intelligence Xidian University Xi’an China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China

Remote sensing scene classification plays a vital role in identifying Earth's surface features. However, traditional methods necessitate vast quantities of labeled training data, which significantly limits their applicability when labeled samples are scarce. To overcome this limitation, remote sensing fewshot classification (RSFSC) methods have arisen as a promising approach to maintaining classification accuracy with minimal data. In this paper, we present DBA-RMCL (Refined Metric Contrastive Learning Framework with Dual-Branch Attention), a novel framework that tackles two fundamental challenges in RSFSC: intra-class diversity and inter-class similarity. Our framework employs a Dual-Branch architecture consisting of Way and Shot branches to reduce intra-class variance while enhancing feature consistency and inter-class separability. The architecture incorporates feature fusion between branches to optimize feature learning and classification performance. We further enhance feature representations through our RSCLR (Refined Siamese Contrastive Learning Representation) module, which implements self-supervised contrastive learning. The framework also includes a CAP-Head(Combine Attention-based Prototypical Head) module with a compositional attention computation unit to model complex relationships between query sets and prototypes. Experimental results on six publicly available datasets demonstrate that DBA-RMCL achieves state-of-the-art performance compared to existing FSC methods. For 5-way 1-shot tasks, our framework attains accuracies of 77.15 +/- 0.34% on NWPU-RESISC45 and 87.33 +/- 0.22% on WHU-RS19. Additionally, we established the first benchmark for few-shot scene classification, MEET-FSL, using the MEET dataset. These outcomes not only underscore the efficacy of our Dual-Branch architecture but also represent a significant advancement in the field of few-shot learning for remote sensing classification. Read more

关键词： Contrastive learning Remote sensing Measurement Feature extraction Few shot learning Training Scene classification Accuracy Computer architecture Robustness dual-branch framework few-shot learning image scene classification remote sensing

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MLMamba: A Mamba-based Efficient Network for Multi-label Remote Sensing Scene Classification

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Du, Ruiqi Tang, Xu Ma, Jingjing Zhang, Xiangrong Jiao, Licheng Xidian University School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education China

As a useful remote sensing (RS) scene interpretation technique, multi-label RS scene classification (RSSC) always attracts researchers' attention and plays an important role in the RS community. To assign multiple semantic labels to a single RS image according to its complex contents, the existing methods focus on learning the valuable visual features and mining the latent semantic relationships from the RS images. This is a feasible and helpful solution. However, they are often associated with high computational costs due to the widespread use of Transformers. To alleviate this problem, we propose a Mamba-based efficient network based on the newly emerged state space model called MLMamba. In addition to the basic feature extractor (convolutional neural network and language model) and classifier (multiple perceptrons), MLMamba consists of two key components: a pyramid Mamba and a feature-guided semantic modeling (FGSM) Mamba. Pyramid Mamba uses multi-scale scanning to establish global relationships within and across different scales, improving MLMamba's ability to explore RS images. Under the guidance of the obtained visual features, FGSM Mamba establishes associations between different land covers. Combining these two components can deeply mine local features, multi-scale information, and long-range dependencies from RS images and build semantic relationships between different surface covers. These superiorities guarantee that MLMamba can fully understand the complex contents within RS images and accurately determine which categories exist. Furthermore, the simple and effective structure and linear computational complexity of the state space model ensure that pyramid Mamba and FGSM Mamba will not impose too much computational burden on MLMamba. Extensive experiments counted on three benchmark multi-label RSSC data sets validate the effectiveness of MLMamba. The positive results demonstrate that MLMamba achieves state-of-the-art performance, surpassing existing meth

关键词： Semantics

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SAR target recognition network based on frequency domain covariance matrix and Riemannian manifold

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Geomatica 2025年第1期77卷

作者： Guo, Zhengxi Hou, Biao Yang, Chen Guo, Xianpeng Wu, Zitong Ren, Bo Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Shaanxi Province Xi'an710071 China

Synthetic Aperture Radar (SAR) image target recognition is a critical problem in remote sensing. Traditional deep learning-based methods often overlook the rich information in the frequency domain of SAR images. Existing frequency domain based approaches typically rely on prior knowledge to select different frequency components or guide network learning, failing to fully exploit the information contained in the frequency domain. We believe that knowledge from the macroscopic high-dimensional space needs to be harnessed to learn about the frequency domain. Therefore, in this paper, we propose an innovative SAR target recognition framework known as FDCM-Net (Frequency Domain Covariance Matrix and Riemannian Manifold Network). Our method integrates the frequency domain information of SAR images with the covariance matrix, revealing the complex relationships between different frequency components and delving into the latent features within the frequency domain. Additionally, we explore the geometric properties of the covariance matrix and design an innovative network architecture based on Riemannian manifold theory. This network effectively captures important information from the frequency domain of SAR images in high-dimensional space while preserving the geometric characteristics of the original data and performing classification. We conducted comprehensive evaluations of FDCM-Net using the MSTAR, OpenSARShip 2.0 and FUSAR-Ship 1.0 datasets. Our results demonstrate that FDCM-Net outperforms previous methods based on spatial and frequency domain baselines in terms of classification performance. © 2025 key laboratory of intelligent perception and image understanding of ministry of education, Xidian University

关键词： Deep learning

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A 3D Self-Awareness Diffusion Network for Multimodal Classification

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IEEE Transactions on Multimedia 2025年

作者： Ma, Mengru Ma, Wenping Jiao, Licheng Li, Lingling Liu, Xu Liu, Fang Yang, Shuyuan Guo, Yuwei Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation School of Artificial Intelligence Xi'an710071 China

As imaging sensor technology in remote sensing has advanced quickly, multimodal fusion classification has become an important research direction in land cover and urban planning classification tasks. While generative models and image classification have greatly benefited from diffusion models, the present ones primarily concentrate on single-modality-driven diffusion processes. Therefore, this paper presents a 3D self-awareness diffusion network (3DSA-DiffNet) for multispectral (MS) and panchromatic (PAN) image fusion classification, which would make it easier to classify heterogeneous data from various sensors. First, in order to model the relationship between multi-channel spectra and multi-pixel spatial distributions as well as samples, respectively, a spatial-spectral joint denoising network (S2 JD-Net) is proposed. It can incorporate the diffusion process into the neural network to enhance the quality of diffusion features. Secondly, to imitate the brain's spatial-spectral coexistence learning mechanism, this work offers a 3D self-awareness module (3DSA-Module) that can learn the weight of each pixel in 3D space, resulting in extraordinarily high feature representation capabilities. Finally, experimental verification demonstrates that the 3D self-awareness diffusion fusion network driven by brain inspiration outperforms more sophisticated approaches on the Xi'an, Huhhot, and Muufl datasets. © 1999-2012 IEEE.

关键词： Urban planning

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Knowledge-Aware Evolutionary Transformer

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IEEE Transactions on Evolutionary Computation 2025年

作者： Zhang, Ruohan Li, Lingling Jiao, Licheng Liu, Fang Liu, Xu Yang, Shuyuan Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Shaanxi Province Xi'an710071 China

With the Transformer architecture achieving impressive results in the vision domain. It has become a current popular research to explore more potentials of Transformer mixed architectures and explore more suitable mixed combinations. In this paper, Transformer classification network is designed and explored by multi-task architecture search algorithm. A new paradigm for multi-task architecture is designed by combining convolution and Transformer. The designed search architecture can combine the respective advantages of convolution and Transformer and can obtain better performance. At the same time, corresponding knowledge-aware multi-task genetic operators are designed to generate offspring individuals. Inter-task and inter-experience knowledge-aware is utilised to facilitate evolutionary convergence. During the search process, the reference evaluation method is utilised to reduce the redundant computation and time during the search process. In the experimental section, the search results are compared with state-of-the-art architectures and search algorithms. The experimental results confirm the effectiveness and high generalisation of the searched architectures. The ablation experimental part proves the effectiveness of the proposed architectural paradigm, genetic operators and reference evaluation. © 2025 IEEE.

关键词： Convolution

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Uncertainty-aware Semi-supervised Learning Segmentation for Remote Sensing images

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IEEE Transactions on Multimedia 2025年

作者： Lu, Xiaoqiang Li, Lingling Jiao, Licheng Liu, Xu Liu, Fang Ma, Wenping Yang, Shuyuan Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation School of Artificial Intelligence Shaanxi Province Xi'an710071 China

Deep learning based remote sensing (RS) image segmentation significantly impacts several real application scenarios. Behind its success, massive labeled data plays an important role. However, annotating high-resolution RS images requires time-consuming and relevant expertise efforts. To address it, many works dive into semi-supervised learning which utilizes raw information embedded in unlabeled data to improve the segmentation model. Nevertheless, previous studies ignore the integrity and effectiveness of the potential context information hidden in RS data. In this work, we propose an uncertainty-aware masked consistency learning (U-MCL) framework that contains an uncertainty-aware masked denoising (U-MD) module and an uncertainty-aware masked image consistency (U-MIC) module. U-MCL initially generates a patch-wise uncertainty map for each unlabeled image during each training iteration, which is then used to derive an adaptive mask ratio for pseudo-label denoising in U-MD. Simultaneously, the uncertainty map is adopted to model a masked unlabeled image for reasoning unseen areas in U-MIC. Consequently, U-MCL is capable of enhancing model performance by engaging in accurate and stable consistency learning while preserving the integrity of the context and employing the context to infer the predictions of the masked regions safely. Extensive experiments on six RS datasets, i.e., ISPRS Vaihingen, FloodNet, MiniFrance, LoveDA, MER, and MSL, demonstrate the superiority of our U-MCL over recent most advanced methods, achieving new state-of-the-art performance under all benchmarks. Code will be available at https://***/xiaoqiang-lu/U-MCL. © 2025 IEEE.

关键词： Self-supervised learning

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Adaptive Complex Wavelet Informed Transformer Operator

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IEEE Transactions on Multimedia 2025年

作者： Li, Xiaotong Jiao, Licheng Liu, Fang Yang, Shuyuan Zhu, Hao Liu, Xu Li, Lingling Ma, Wenping Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Joint International Research Laboratory of Intelligent Perception and Computation School of Artificial Intelligence Shaanxi Xi'an710071 China

Visual transformers have achieved great success in representation learning. This is mainly due to efficient token dependency modeling via self-attention. However, the computational burden increases sharply as the input pixels increase. Although recent Fourier-based global frequency-domain mixing methods attempt to improve the efficiency of transformers for high-resolution image inputs, the Fourier operator has limited ability to capture the local geometric structure. Complex wavelets can perform local attention in both the spatial domain and the frequency domain. Therefore, we propose the complex wavelet informed transformer operator that uses the real and imaginary wavelets of the dual-tree complex wavelet transform to simulate the interaction in the attention kernel. In order to further reduce the computational burden of operators, we introduce an adaptive local block shared attention mechanism in the channel domain for our wavelet informed operators. Further, we construct the deep multi-head operator network consisting of a hybrid stack of complex wavelet informed transformer operators and self-attention layers. This enables the Transformer to more sparsely capture multi-scale and multi-directional structured features in the process of learning dependencies. Extensive experimental results show that our adaptive complex wavelet informed transformer operator under the Transformer architecture achieves highly competitive accuracy performance on multiple image classification benchmark datasets. And the proposed operators can be flexibly and effectively migrated to vision tasks in dynamic video scenarios. Codes are available at https://***/Dawn5786/ACWI-Former. © 1999-2012 IEEE.

关键词： Frequency domain analysis

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Change Knowledge-Guided Vision-Language Remote Sensing Change Detection

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Wang, Jiahao Liu, Fang Jiao, Licheng Wang, Hao Li, Shuo Li, Lingling Chen, Puhua Liu, Xu Ma, Wenping Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Joint International Research Laboratory of Intelligent Perception and Computation School of Artificial Intelligence Shaanxi Xi’an710071 China

Remote sensing image change detection plays a critical role in applications like video surveillance and geographic information systems. However, existing binary and semantic change detection methods often rely solely on visual information, neglecting language information, which limits interpretability and the ability to provide specific change details. This work proposes the Change Knowledge-Guided Vision-Language Remote Sensing Change Detection (CKCD) method to address these limitations. By introducing change knowledge as language information, CKCD enhances semantic understanding and change detail representation. A Cross-Modal Affinity (CMA) module is designed to effectively fuse visual and textual features, improving information complementarity and fusion coherence. CKCD further enhances data utilization efficiency by merging change area detection and change category information into a single output through endto- end learning. This design reduces redundant data representations and simplifies the detection process, leading to a more compact and efficient use of the input data without requiring additional branches or multiple output heads. Experimental results demonstrate consistent performance improvements over traditional methods across multiple change detection datasets. © 1980-2012 IEEE.

关键词： Semantics Remote sensing Visualization Feature extraction Transformers Accuracy Training Artificial intelligence Robustness Head

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