Domain generalization-based hyperspectral image classification methods have achieved promising results in recent years. However, these studies seldom consider the issue of small sample in the source domain. In practical applications, manually annotating hyperspectral images is difficult, so labeled samples in the source domain may be scarce. Existing models have limited feature extraction capability and poor generalization performance in scenarios with limited labeled samples. To address the limitations of existing methods on small sample data of the source domain, a novel approach, Progressive Multiscale Generator for Domain Generalization (PMGDG), is proposed in this paper. The PMGDG employs a progressive multiscale generator comprising a series of sub-generators with paired sub-discriminators. The channel dimension of generated samples grows gradually from the first layer to the last layer. Then, the Classifier network is trained on both the original samples and the generated samples with different distributions to enhance its generalization performance. Additionally, we introduce a hierarchical optimization approach to stabilize the training process. Extensive experiments are conducted on three public hyperspectral image cross-domain datasets:Houston, Pavia, and HyRANK. The experimental results demonstrate that, compared to existing domain generalization methods for hyperspectral image classification, the proposed approach significantly improves classification performance under small sample. The code is available from the website: https://github.com/adwfdawd/PMGDG.

Specific emitter identification (SEI) is a crucial task in various applications such as wireless communications and radar systems. The low-pass nature of vanilla Transformers hinders the extraction of high-frequency fingerprint features, resulting in poor SEI performance. Moreover, the introduction of additional high-frequency sensing structures can increase the computational efficiency of the already computationally intensive Transformer. To address these issues, we propose a high-frequency enhanced and low-complexity Transformer named HET. The framework integrates a multihead low-complexity self-attention (MLSA) module, a high-frequency enhanced connection, and a multihead high-frequency enhanced low-complexity self-attention (MESA) module. The MLSA module reduces the computational complexity by key and value mapping. The MESA and high-frequency enhanced connection module capture high-frequency information by reconstructing the low-frequency and high-frequency components of the features. We construct three HET variants, namely, $\text {HET}_{n}$ , $\text {HET}_{u}$ , and $\text {HET}_{m}$ , based on different enhancement methods and positions using $\text {MESA}_{n}$ , $\text {MESA}_{u}$ , and $\text {MESA}_{m}$ , respectively. Extensive experiments are conducted on the XSRP, ADS-B, and Wi-Fi datasets to evaluate the proposed models, demonstrating their competitive accuracy and faster throughput compared with popular methods. Theoretical proofs of high-frequency suppression and frequency response results confirm that the proposed framework has more gain for high-frequency information in SEI. Code is available at: https://github.com/zhailei-zl/HETmodel.

This paper studies grid-free point process modeling under varying fluid parameters. Existing methods rely on grid-based approaches or fixed parameters, making it challenging to handle complex nonlinear dynamics and out-of-distribution (OOD) scenarios. To address this, we propose Adaptive Perturbation Graph ODE (AGODE), a novel framework that integrates three key innovations: (1) an adaptive conditioning mechanism for physical parameter adaptation(2) a continuous graph neural ODE for spatiotemporal evolution modeling, and (3) a perturbation module with mutual information maximization for uncertainty quantification. AGODE employs graph neural networks to encode unstructured point cloud data into latent dynamics governed by neural ODEs, where physical parameters are injected through context-aware conditioning vectors. The perturbation module generates diverse trajectory samples by introducing stochastic noise during ODE integration, while contrastive learning aligns predictions with physical contexts to filter implausible outcomes.

Applying self-supervised deep learning improves the processing speed and accuracy of automatic modulation recognition (AMR). It reduces the dependence of previous deep networks on many labeled samples. However, affected by an incomplete signal representation modes set, previous models do not fully utilize the multiview property of signals in self-supervised learning. To deal with this issue, a hybrid-view contrastive model for AMR is proposed in this article based on self-supervised learning framework. First, star video is proposed to complete the set of signal representation modes. Next, a self-supervised learning framework based on hybrid-view contrastive learning, hybrid-view self-supervised framework (HVSF), is established to fully extract the signal features, where signals are augmented across views, including the discrete sequence, image, and video format. Considering the view-exclusive information loss and the model complexity, a weakly contrastive strategy and a Transformer-based view-shared feature extractor are finally constructed. Evaluation on four standard datasets demonstrates that the proposed model, HVSF, outperforms both the self-supervised models and supervised models, affirming its superior performance and stability.

Automatic modulation classification (AMC) has a wide range of applications in both civilian and military fields, such as industrial Internet of Things (IIoT) security, communication spectrum management, and military electronic countermeasures. However, label mislabeling often occurs in practical scenarios, significantly impacting the performance and robustness of deep neural networks (DNNs). In this article, we propose a meta-learning guided label noise distillation method to enhance the robustness of AMC models against label noise or errors. Specifically, we propose a teacher-student heterogeneous network (TSHN) to discriminate and distill label noise. Following the notion that labels represent information, a teacher network, utilizing trusted few-shot labeled samples, reevaluates and corrects labels for a considerable number of untrusted labeled samples through meta-learning. By dividing and conquering untrusted labeled samples according to their confidence levels, the student network learns more effectively. Additionally, we propose a multiview signal (MVS) method to further enhance the performance of hard-to-classify categories with few-shot trusted labeled samples. Extensive experiments on the RadioML2016 and HisarMod2019.1 data sets demonstrate that our methods significantly improve accuracy and robustness in signal AMC across diverse label noise scenarios, including symmetric, asymmetric, and mixed label noise. For example, compared to the baseline convolutional neural network with the cross-entropy loss, our proposed TSHN achieves a remarkable 1.26% to 36.84% accuracy improvement under symmetric label noise and 0.12% to 38.59% accuracy improvement under mixed label noise. Moreover, TSHN exhibits greater robustness to varying label noise rates compared to existing methods.

Deep neural networks (DNNs) have been successfully applied in the remote sensing semantic segmentation. However, training DNNs requires a large number of densely labeled samples, which is laborious and time-consuming. Sparsely supervised semantic segmentation (SSSS) can train deep segmentation networks using only sparse annotations. In this paper, we propose a negative class guided spatial consistency network (NCG-SCNet) for semantic segmentation with sparse annotations. Specifically, we introduce a spatial consistency enhancement module (SCEM) to enhance network features by non-linearly combining spatially similar features. Thus, it could provide better representations of the boundaries and the shape of the target. Additionally, a channel compression module (CCM) is proposed to reduce channel redundancy while preserving the network’s feature extraction capability. A negative class guided loss function (NCG Loss) is constructed to provide extra supervisory information, where the negative classes are defined as the classes with lower probability in the prediction. Extensive experiments on two widely used remote sensing datasets show that the proposed NCG-SCNet outperforms the comparison methods.

Cross-scene hyperspectral image (HSI) classification faces spectral-spatial feature distribution shifts resulting from cross-domain heterogeneity, which has become a critical challenge that urgently needs resolution in the field of remote sensing intelligent interpretation. To address this distribution shift, the mainstream approach is Domain Generalization (DG). However, existing HSI DG methods primarily focus on inter-class separability, while paying relatively less attention to cross-domain transferability. To overcome the limitation that existing methods mainly focus on inter-class separability, this study proposes a cross-scene HSI classification network, termed DPSKDnet. By synergistically employing dynamic perturbation-based destylization and self-knowledge distillation modeling mechanisms, DPSKDnet constructs domain-invariant representations with strong generalization capabilities in the feature space. Specifically, this study first builds a generator based on dynamic perturbation destylization to mine source domain (SD) invariant features and generate extended domain (ED) samples. Subsequently, a Fourier Augmentation Module is utilized to optimize the frequency domain representations of the SD, ED, and their combination-generated intermediate domain, obtaining frequency-enhanced representations. To effectively improve the model’s ability to capture domain-invariant features, a sample pair distillation loss is devised. This loss, informed by multi-domain mixed data input, guides the discriminator in online self-supervised learning. The overall accuracy of this method on Loukia, Houston2018, and Pavia Center increased by 0.48%, 0.81%, and 1.5%, respectively, compared to state-of-the-art methods. The code is available on the website: https://github.com/Yuhang-Hong/TGRS_DPSKDnet.

In the field of multimodal remote sensing image (MRSI) classification, self-supervised learning (SSL) algorithms have demonstrated significant advantages, particularly in scenarios with limited labeled samples. Existing SSL methods typically use auxiliary tasks within either contrastive or generative frameworks, focusing on discriminative or structural information separately. In this article, we propose a novel hybrid SSL paradigm, discriminative autoencoder with Gaussian selective patch (DAE-GSP) for MRSI classification. The DAE framework integrates contrastive learning with the masked image modeling (MIM) technique, allowing for simultaneous learning of structural information and discriminative representations from images. Furthermore, a cross-attention-based data-level fusion strategy is introduced during pretraining stage to enhance intermodal interactions, thereby improving the effectiveness of modality fusion. In addition, we propose a novel Gaussian selective patch (GSP) strategy, addressing the limitations of traditional square patch selection methods. Combined with self-supervised auxiliary tasks, this strategy facilitates the improved integration of multiple modalities and encourages the model to capture essential semantic information. Extensive experiments conducted on three public datasets (Houston2013, Augsburg, and Berlin) demonstrate the effectiveness of the proposed approach. With only ten labeled training samples per class, the proposed method achieves overall accuracy (OA) of 90.15%, 82.64%, and 71.03% on the Houston2013, Augsburg, and Berlin datasets, respectively, indicating improvements of 1.31%, 1.22%, and 1.48% over state-of-the-art methods.

In the field of remote sensing self-supervised learning, the self-supervised learning paradigm based on masked image modeling (MIM) effectively promotes the learning of structural and contextual semantic information in images. However, the random masking strategies employed in past studies have not effectively utilized the distribution of semantic information within images. Furthermore, overly simple reconstruction tasks cannot effectively improve self-supervised learning performance. To address these challenges, this paper introduces a gradient-guided activation-aware autoencoder (GA-MAE). Specifically, this approach computes and captures the spatial spectral activation map (SSAM) with semantic distribution information during the process of backpropagation of spatial-spectral reconstruction loss in the pre-training phase, thereby enabling masking operations on similar visual patches with higher reconstruction loss. Additionally, we introduce an Activation-Aware Self-Attention mechanism (ASA) that adjusts the self-attention dependencies by utilizing the weight information of visual patches provided by SSAM. Extensive experiments conducted on three public datasets (Houston2013, Augsburg, and Berlin) demonstrate the effectiveness of the proposed approach. With only ten labeled training samples per class, the proposed method achieves anoverall accuracy (OA) of 91.24%, 84.69%, and 73.43% on the Houston2013, Augsburg, and Berlin datasets, respectively, indicating improvements of 1.09%, 2.05%, and 2.40% over state-of-the-art methods.

In recent years, superpixel-based graph convolutional networks (GCNs) have drawn increasing attention within the hyperspectral image (HSI) classification community. Due to the high-dimensional property of HSI, establishing a high-quality and accurate initial graph is still a great challenge for the superpixel-based GCN methods. In addition, the lack of high-level semantics within the superpixel-based node features leads to poor classification performance of the model, especially in scenarios with limited labeled samples. To tackle these problems, we propose a novel approach called the dual-semantic graph convolution network (DSGCN) for HSI classification in this article. Specifically, our method employs superpixel segmentation to construct graph nodes with semantic structure information, treating each superpixel in the HSI as a node within the graph. We design a superpixel-level autoencoder that integrates with the initial graph to update the edge weights. With the learnable edge weights, our model can adaptively learn robust spatial semantic (SS) information from HSI. Additionally, we introduce a spectrum-flow (SF) module to extract global spectral semantic variation information. To further enhance the nonlinearity capability of GCN, we replace the traditional linear layer with a novel network layer referred to as Kolmogorov Arnold networks (KANs) during the node representation phase. In addition, we develop a memory-efficient residual spectral attention (MERSA) module that adapts to the full-batch training manner in the convolutional neural network (CNN) branch to supplement fine-grained pixel-level features. Extensive experiments conducted on four benchmark datasets demonstrate that our proposed DSGCN significantly outperforms several state-of-the-art methods, particularly when using a small amount of labeled data.

Recently, deep neural networks (DNNs) based emitter recognition or identification has received increasing interest. However, most of them are purely data-driven and require a large number of labeled instances. In this paper, a new Knowledge Driven Signal Transformer (KDSiT) is proposed, which introduces the knowledge graph (KG) into a signal Transformer (ST) model for accurate emitter recognition in real-world scenarios. On the one hand, KDSiT use a unified multimodal Transformer structure to explore the latent long-range dependencies in signals, and capture the subtle differences of emitters. On the other hand, KDSiT introduces domain knowledge, such as relationships and attributes between emitters, by constructing an emitter knowledge graph. By combining the powerful feature learning capability of DNNs with the rich semantic information in KG, KDSiT can extract more discriminative features of emitters from multimodal learning, to improve the identification accuracy in degraded environments. Extensive experiments are conducted, and the results prove the superiority of KDSiT over its counterparts, especially in the case of low signal-to-noise ratio (SNR), incomplete signals, and a limited number of labeled instances.

The proliferation of Internet of Things (IoT) devices generates substantial data that supports deep learning, significantly advancing intelligent specific emitter identification (SEI) technology. However, challenges such as labeling costs and privacy concerns limit the availability of labeled samples, thereby constraining deep model training. To address this problem, this paper focuses on enhancing the data manifold structure through deep feature information, proposing a semi-supervised SEI method named SSME. A well-structured manifold makes the model capture underlying patterns and relationships within the data more effectively, leading to more accurate and generalizable classification boundaries. First, to maximize the use of supervision information from limited labeled samples, we design a supervised cross-class contrastive (SCCC) loss, which increases the feature distance between anchor samples and cross-class samples based on their labels, achieving better manifold separation of different categories. Second, we propose an instance neighborhood matching regularization (INMR) loss that captures the neighborhood of weakly and strongly augmented samples of unlabeled instances within the feature space. By aligning these neighborhood representations, neighborhood-to-neighborhood consistency learning is achieved, enhancing the structural consistency and smoothness of local manifolds. Evaluated on ADS-B and XSRP datasets across diverse settings, our method demonstrates superior performance over existing approaches. Notably, even with only five labeled samples per class, it surpasses supervised baselines by 24.82% and 12.55% on the respective datasets.

Recently, contrastive clustering (CC) has exhibited considerable advantages for unsupervised specific emitter identification (USEI). However, emitter signals consist of connotative information, fingerprint information, and noise. As meaningful information transmitted by signals, connotation information hinders the extraction of discriminative fingerprints from emitter signals. In this paper, a novel Similarity Connotation Masking guided Contrastive Clustering (SC3) method is proposed for USEI. First, SC3 generates pairs of emitter signal samples with connotative exclusivity through the connotation masking module (CMM). Second, a translation-invariant multi-scale fingerprint extractor (TIMFE) with a wide receptive field to efficiently extract and decouple fingerprints. By separating the connotation information from the sample pairs in CC and learning robust features via TIMFE, SC3 could obtain accurate radio frequency fingerprints (RFFs) from degraded emitter signals. Extensive experiments are conducted on several datasets, including CBRS, Wi-Fi and XSRP datasets. The numerical results indicate that the proposed SC3 method consistently outperforms state-of-the-art algorithms regarding four clustering indicators. Code available: https://github.com/2017212073/SC3.

With the rapid development of wireless communications, industrial electromagnetic environments are facing challenges in terms of spectrum scarcity and cyberspace threats. Moreover, the coexistence of various types of radio signals within the same frequency band may cause signal distortion and degrade the quality and efficiency of communication. To effectively address these challenges, a novel temporal–spectral feature fusion network (TSFFN) for radio signal classification (RSC) is proposed. TSFFN adopts a Cutmix-based temporal–spectral fusion and an attention-based multiview feature fusion mechanism. These mechanisms automatically learn and merge spectrogram, temporal–spectral, and time-domain features by effectively combining temporal and spectral information into high-dimensional representations. This augmentation enhances the network's ability to discriminate different radio signals, enabling accurate spectrum sensing and signal identification for effective spectrum management. Experimental results on five datasets demonstrate the effectiveness of our approach in enhancing RSC performance across diverse industrial scenarios.

Cracks are critical defects in concrete structures, traditionally identified through human inspection. However, computer vision techniques, especially convolutional neural networks (CNNs), offer promising solutions for automated detection. Driven by this trend, this study proposesCrackVisionX, a state-of-the-art deep learning framework for classifying binary concrete cracks.CrackVisionXlies in its integration of advanced CNN architectures, ResNet50, MobileNet_v3_large, DenseNet121, and EfficientNetB0, with extensive hyper-parameter tuning. This integration optimizes crack detection accuracy while maintaining low model complexity and reducing bias, making it suitable for real-time applications. Furthermore, the framework introduces a robust data augmentation strategy that effectively addresses dataset imbalances, enhancing model generalization across diverse domains. Additionally,CrackVisionXemploys comprehensive preprocessing on the METU and SDNET2018 datasets to create six domains: Bridge Deck, Wall, Pavement, SDNET2018, METU, and METU & SDNET2018. The framework’s performance is thoroughly evaluated and benchmarked against state-of-the-art methods, utilizing diverse metrics to improve the detection of cracks in concrete structures. EfficientNetB0, a core component of the framework, demonstrated superior performance with exceptional test accuracies of up to 99.71%, 99.78%, 99.55%, 99.89%, 99.98%, and 99.92% for Bridge Deck, Wall, Pavement, SDNET2018, METU, and METU & SDNET2018, respectively. Moreover, we evaluated the robustness ofCrackVisionXusing images contaminated with different types and intensities of noise, demonstrating its reliability and effectiveness. This balance between high accuracy and computational efficiency confirms the framework’s potential for practical deployment. The experimental results emphasize the transformative potential of deep learning in construction safety and structural health monitoring.

Recently, the multiscale problem in computer vision has gradually attracted people's attention. This article focuses on multiscale representation for object detection and recognition, comprehensively introduces the development of multiscale deep learning, and constructs an easy-to-understand, but powerful knowledge structure. First, we give the definition of scale, explain the multiscale mechanism of human vision, and then lead to the multiscale problem discussed in computer vision. Second, advanced multiscale representation methods are introduced, including pyramid representation, scale-space representation, and multiscale geometric representation. Third, the theory of multiscale deep learning is presented, which mainly discusses the multiscale modeling in convolutional neural networks (CNNs) and Vision Transformers (ViTs). Fourth, we compare the performance of multiple multiscale methods on different tasks, illustrating the effectiveness of different multiscale structural designs. Finally, based on the in-depth understanding of the existing methods, we point out several open issues and future directions for multiscale deep learning.

Open-set modulation classification (OMC) of signals is a challenging task for handling "unknown" modulation types that are not included in the training dataset. This article proposes an incremental contrastive learning method for OMC, called Open-ICL, to accurately identify unknown modulation types of signals. First, a dual-path 1-D network (DONet) with a classification path (CLP) and a contrast path (COP) is designed to learn discriminative signal features cooperatively. In the COP, the deep features of the input signal are compared with the semantic feature centers (SFCs) of known classes calculated from the network, to infer its signal novelty. An unknown signal bank (USB) is defined to store unknown signals, and a novel moving intersection algorithm (MIA) is proposed to dynamically select reliable unknown signals for the USB. The "unknown" instances, together with SFCs, are continuously optimized and updated, facilitating the process of incremental learning. Furthermore, a dynamic adaptive threshold (DAT) strategy is proposed to enable Open-ICL to adaptively learn changing signal distributions. Extensive experiments are performed on two benchmark datasets, and the results demonstrate the effectiveness of Open-ICL for OMC.

The past decade has witnessed the rapid development of deep neural networks (DNNs) for automatic modulation classification (AMC). However, most of the available works learn signal features from only a single domain via DNNs, which is not reliable enough to work in uncertain and complex electromagnetic environments. In this brief, a new cross-domain signal transformer (CDSiT) is proposed for AMC, to explore the latent association between different domains of signals. By constructing a signal fusion bottleneck (SFB), CDSiT can implicitly fuse and classify signal features with complementary structures in different domains. Extensive experiments are performed on RadioML2016.10A and RadioML2018.01A, and the results show that CDSiT outperforms its counterparts, particularly for some modulation modes that are difficult to classify before. Through ablation experiences, we also verify the effectiveness of each module in CDSiT.

With the advancement of cognitive radio technology (CRT) in radio communication networks, deep learning (DL) has become instrumental in enhancing spectrum efficiency. However, supervised DL methods demand extensive labeled data and incur high manual costs. Consequently, practical applications of CRT increasingly necessitate techniques capable of learning robust representations from large volumes of unlabeled data. Although recent DL advancements have driven the use of self-supervised learning (SSL) in CRT through time-domain contrastive methods, these approaches fall short in extracting high-level spectral representations due to their neglect of time-frequency features. To address these limitations, a generative SSL framework is proposed for CRT applications. First, SSL pretraining is conducted in the time-frequency domain by reconstructing masked spectrograms using a Masked Autoencoder. Then, to recover the spectrogram under extreme radio conditions, mutual information maximization is employed to extract high-level spectral information obscured by noise patterns. Additionally, an attention-based channel-spectrum fusion module is designed to automatically extract and integrate features from the channel and spectral domains. The feasibility of the proposed framework is evaluated across multiple downstream tasks on four public datasets. Experimental results demonstrate that the proposed framework significantly outperforms existing methods in various downstream tasks.

Most existing wideband signal detection and recognition (WSDR) methods rely on diverse, large-scale, and well-labeled training data, which are often difficult to obtain in practical application scenarios such as non-cooperative environments and novel signaling regimes. In this article, we propose a method for constructing a virtual signal large model (VSLM) and applying it to tackle the WSDR challenge under few-shot or even cross-domain few-shot scenarios. Firstly, we design two plug-and-play modules, virtual sample generation (VSG) and virtual category generation (VCG), for VSLM, respectively. VSG simulates the local and overall relationship between the burst signal and the constant signal, which is mainly completed by extracting time-frequency meta-block and data enhancement. Based on VSG and the multi-label concept, we further create virtual novel categories by injecting customizable semantic information into meta-blocks. Then, we further propose a dual decoupled network (DDN) to train the VSLM. DDN enhances signal details by decoupling low gray values (DLGV) in time-frequency representation, and alleviates conflicts during multi-task joint optimization by decoupling spectrum localization and signal classification. Finally, based on the wideband spectrogram dataset, extensive experiments have validated that our proposed methods can significantly improve the performance of WSDR under few-shot conditions.

Deep learning methods have achieved outstanding performance in various signal tasks. However, due to degraded signals in real electromagnetic environment, it is crucial to seek methods that can improve the representation of signal features. In this paper, a Singular Value decomposition-based Attention, SVA is proposed to explore structure of signal data for adaptively enhancing intrinsic feature. Using a deep neural network as a base model, SVA performs feature semantic subspace learning through a decomposition layer and combines it with an attention layer to achieve adaptive enhancement of signal features. Moreover, we consider the gradient explosion problem brought by SVA and optimize SVA to improve the stability of training. Extensive experimental results demon-strate that applying SVA to a generalized classification model can significantly improve its ability in representations, making its recognition performance competitive with, or even better than, the state-of-the-art task-specific models.

Fault diagnosis is crucial in mechanical prognostics and health management. However, fault features extracted from single-sensor data are limited in complex operating environments. Extracting complementary and robust fault features from multi-sensor monitoring data is essential, especially under limited labeled samples. Leveraging the advantages of self-supervised learning, we propose a novel cross-sensor self-supervised learning (CSSL) method for rotating machinery fault diagnosis under limited sample conditions. Our method employs contrastive learning across multiple sensors, including both intra-sensor and inter-sensor contrastive learning, to derive robust cross-sensor fault representations. The efficacy of our approach is substantiated on two benchmark datasets, revealing superior classification performance. Furthermore, the experimental results under various operating conditions demonstrate outstanding performance and solid robustness.

Vision Transformer (ViT) has achieved great success in the field of computer vision since it was proposed, and there have been many works applying ViT based models to remote sensing scene classification (RSSC) tasks. The proposal of Pyramid Vision Transformer (PVT) greatly reduces the calculation amount of the ViT while maintaining accuracy. But PVT did not utilize multi-scale information in remote sensing (RS) scenes, which is crucial for RSSC. This paper proposes a multi-scale sparse transformer (MST) based on PVT. MST enables the network to learn multi-scale representations of RS scenes through spatial reduction implementations at different scales. In addition, we employ sparse operations to adaptively guide the model’s attention towards semantically relevant regions during self-attention computation, thereby reducing interference from semantically irrelevant areas. Experiments conducted on the UCM and AID datasets demonstrate the outstanding performance of the proposed MST.

Available deep learning (DL) based pansharpening methods primarily extract spectral information and spatial information from panchromatic (PAN) and multispectral (MS) images, respectively. This hinders the efficient derivation and exploitation of the potential spectral-spatial information, thereby degrading the quality of generated high-resolution multispectral (HRMS) images. In this paper, we propose a novel multi-stage dual-domain guided attention pansharpening network (MDGAPN), aiming to fully capitalize on the spectral and spatial information contained in both the PAN and MS images. Firstly, to enhance the feature extraction, we introduce two subnetworks: the progressive spectral feature extraction subnetwork (PSPeN) and progressive spatial feature extraction subnetwork (PSPaN), which are devised to extract both spatial and spectral information from the input PAN and MS images. Wherein, the frequency domain (FD) and intensity domain (ID) features of the PAN and MS image are leveraged as the guidance to improve the efficiency of feature extraction. Then, a joint spatial-spectral attention feature fusion module and a multi-stage residual reconstruction module are devised to efficiently harness the extracted spatial and spectral information. Finally, experiments are conducted to assess the effectiveness of our proposed MDGAPN.

Time series analysis finds wide applications in fields such as weather forecasting, anomaly detection, and behavior recognition. Previous methods attempted to model temporal variations directly using 1D time series. However, this has been quite challenging due to the discrete nature of data points in time series and the complexity of periodic variation. In terms of periodicity, taking weather and traffic data as an example, there are multi-periodic variations such as yearly, monthly, weekly, and daily, etc. In order to break through the limitations of the previous methods, we decouple the implied complex periodic variations into inclusion and overlap relationships among different level periodic components based on the observation of the multi-periodicity therein and its inclusion relationships. This explicitly represents the naturally occurring pyramid-like properties in time series, where the top level is the original time series and lower levels consist of periodic components with gradually shorter periods, which we call the periodic pyramid. To further extract complex temporal variations, we introduce self-attention mechanism into the periodic pyramid, capturing complex periodic relationships by computing attention between periodic components based on their inclusion, overlap, and adjacency relationships. Our proposed Peri-midFormer demonstrates outstanding performance in five mainstream time series analysis tasks, including short- and long-term forecasting, imputation, classification, and anomaly detection.

With the continuous growth of Internet of Things (IoT) deployments, various wireless devices and communication technologies coexist in industrial environments resulting in a crowded and dynamic frequency spectrum. Efficient spectrum sensing becomes essential to mitigate interference, enhance the communication reliability, and ensure the seamless coexistence of diverse wireless technologies. However, the extremely dense and varied signals bring challenges for the precise detection, estimation, and recognition of signals in complex and varied signal environments. The residual dilated network (RDN) and horizontal shift attention (HSA) mechanisms presented in this article offer innovative solutions to the challenges posed by this intricate spectrum landscape. Through multiscale dilated convolution and attention mechanisms, our approach aims to capture and locate the signals precisely, enabling enhanced spectrum utilization within IoT applications. Extensive experiments are conducted on the two data sets, and the results show that our proposed method can automatically extract discriminative features of signals, thereby improving the detection accuracy and recall rate in spectrum sensing. In addition, the probability of false negatives and the inference time can also be reduced simultaneously.

In recent years, data-driven deep learning methods have significantly improved the performance of automatic modulation classification (AMC). However, labeling the vast number of signal samples obtained in a complex electromagnetic environment is challenging due to data security concerns and the drain on manpower and material resources. The scarcity of labeled samples constrains the applicability of these methods. In this article, an ensemble SigMatch (ESM) semi-supervised AMC method is proposed to fully leverage the unlabeled modulated signals. First, a SigMatch (SM) semi-supervised AMC framework is proposed, combining pseudo-labeling, consistency regularization, and modulated signal augmentation for direct identification of raw timing signals. Three different types of signal augmentation methods are investigated through mathematical analysis of the signal model. Second, based on SM and multiview learning, the ESM method is proposed to further enhance the performance of semi-supervised AMC through consistency learning of multiple augmentation views of unlabeled signals. A multiview consistency loss is designed in ESM, with additional data augmentation as complementary views. Multiple perturbed views are guided by the same sample to achieve consistent classification through a shared classification model, thus achieving more robust feature representation. Our method demonstrates remarkable performance on data sets RML2016.10A and RML2016.04C, especially with few labeled samples. On RML2016.10A, with only 110 labeled samples, the ESM enhances the overall classification accuracy from 35.77% to 70.44% compared with supervised learning.

In the non-cooperative electromagnetic environment, new radar emitters will emerge unexpectedly during the test phase, which brings the “Open-Set” Radar Emitter Recognition (OS-RER). Conventional classifiers cannot identify new radar emitters that do not exist in the training dataset. Therefore, in this paper, a novel Deep Metric Auto-Encoder (DMAE) is proposed for OS-RER. In DMAE, deep metric learning learns new non-linear mappings in the metric space to measure the similarity between instances. The dual-path deep auto-encoder is designed to reduce the open space risk by learning a low-dimensional manifold and a discriminative representation of known instances. Specifically, DMAE models known classes, and measures class belongingness through the reconstruction error of the AE and the entropy of the classifier. The deep metric network learns a more precise distance metric by minimizing the distance between the known class instances and the corresponding reconstruction. To accurately detect unknown instances, the classifier and the deep metric network are used together to preliminarily detect unknown instances. Finally, the detected unknown instances are used to further train the classifier to recognize the radar emitter in the open-set scenarios. The DMAE learns the discriminative representation through end-to-end learning. Extensive experiments conducted on real radar datasets and simulated radar datasets show that DMAE can identify unknown emitters and significantly outperforms existing open-set classification methods.

Cross-domain classification of hyperspectral data is a critical challenge in remote sensing, especially when labels are unavailable in the target domain. Deep learning-based domain adaptation (DA) methods have been widely used in recent years. However, curren methods primarily focus on the global domain structure of the source and target domains when considering domain adaptation, neglecting the subdomain structure within each class. Additionally, current methods directly employ predicted outputs without further exploring the confidence level of the target domain samples. These limitations lead to confusion in domain adaptation and hinder effective feature selection in neural networks. In this paper, we propose the Pseudo-Label-Assisted Subdomain Adaptation (PASDA) method, which addresses these limitations by jointly considering the subdomain structure of the source and target domains and adopting a sample selection strategy. PASDA aligns the subdomains while learning domain-invariant features as a foundation. Furthermore, it selects high-quality pseudo-labeled samples from the target domain to enhance the learning of domain-invariant features. For generating pseudo-labels in the target domain, we employ the Reweighted Pruning Label Propagation (RPLPA) strategy to reweight the output of the predicted target domain. Finally, the high-confidence samples with pseudo-labels are selected to finetune the network. The entropy regularized dual classifier constraint is introduced to enhance the discriminative feature extraction ability for the target domain. Extensive experiments on three public HSI cross-domain datasets, Pavia, Houston, and HyRANK, using overall accuracy (OA), average accuracy (AA) and kappa coefficient (Kappa) as the evaluation indicators of classification performance, demonstrate the superiority of our method. Compared with the existing state-of-the-art (SOTA) unsupervised domain adaptation (UDA) methods, our method improves OA by 2% and AA by 4%.

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.

Heterogeneous object-level change detection (CD) aims to detect the state of the objects and whether they have changed from multitemporal multimodal data. In this article, a new cross-modality interactive change detector (CICD) is proposed for object-level CD from multitemporal optical and synthetic aperture radar (SAR) images. The CICD consists of a backbone, cross-modal interactive module (CIM), neck, and head. CIM is designed to work with features extracted from modalities by the backbone network, enabling it to identify more changes in objects. Moreover, to address data imbalances in change categories caused by variations in satellite revisit cycles and aircraft flight plans, we introduce a heterogeneous class balanced module (HCBM). An eliminate adversarial network (EAN) is constructed as the main component of the HCBM. It is used to eliminate objects to augment images in which objects appear in only one temporal instant, thus reducing imbalances in the dataset. Extensive experiments are conducted on the multimodal object-level change dataset (MOCD), and the results show that CICD can achieve state-of-the-art performance.

Deep learning models (algorithms) have demonstrated their superior performance in interpreting Earth science and remote sensing data. However, adversarial examples generated with perturbations imperceptible to humans could render deep learning algorithms ineffective. This significant vulnerability of deep learning models, thus, inspires the exploration of defense methods resistible to adversarial examples. Although numerous countermeasures against adversarial examples have been proposed, the design of a universally applicable defense method across multiple scenarios still remains to be explored. In this study, we propose an effective denoising diffusion-based defense restore network (D3R-Net) based on the denoising diffusion model from the perspective of adversarial restoration, which transforms the adversarial examples into clean samples. Utilizing a highly effective denoising diffusion probabilistic model (DDPM), our D3R-Net transforms input adversarial examples into a state of noise, where diverse forms of adversarial noise transition into Gaussian noise. Subsequently, it captures semantic information through a series of iterative denoising steps. The pixel distribution of adversarial examples is restored in the proposed network to match the original distribution, enabling the classifier to identify adversarial examples correctly. Furthermore, we introduce a combined filtering module to preserve the semantic information of the original image, thereby further enhancing the defensive performance. Instead of modifying the model structure or excluding suspected samples, the proposed method restores the adversarial examples, making it simple yet effective and applicable to a broader range of scenarios. Extensive experiments are conducted on four benchmark datasets, and the results demonstrate that D3R-Net has significant defense capabilities against known and unknown attacks. Our source code is available athttps://github.com/SIM-xidian/D3R-Net.

Most existing polarimetric synthetic aperture radar (PolSAR) image classification methods are only valid under the assumption of identical imaging platforms and terrain categories for both training and test sets. To overcome this limitation, we propose a meta-graph representation learning (MGRL) method for PolSAR image classification with cross-platform and cross-category implementation. First, an integrated network is developed to learn the global-local representations of PolSAR images, which consists of a trumpet convolutional network (TCN) to learn the local scattering features of pixels and a graph convolutional network (GCN) for modeling the global structure of polarization information. Then, a comprehensive and transferable embedding of pixels is derived by collaborative optimization on multiple meta-learning tasks, which enables MGRL to recognize new classes not seen during training. Thus, the learned transferable representations can be quickly adapted to cross-platform and cross-category tasks with few labeled samples. Extensive experiments on several live airborne and spaceborne PolSAR datasets validate the effectiveness and advantages of MGRL over its counterparts.

With the maturity of big data and computing power, deep learning has provided an end-to-end efficient solution for fault diagnosis of rotating machinery. However, the diagnosis performance is commonly affected by complex working environment and limited labeled samples. While considering these undesirable effects and borrowing from multisource fusion techniques, we propose a novel fault diagnosis method based on cross-sensor correlative feature learning and fusion. First, global–local temporal encoder is utilized to learn the time-domain features of multiple sensor data. Meanwhile, time–frequency encoder is performed to obtain the corresponding time–frequency domain features. Then, features of the two modes are fused to get the initial results. Finally, they are put through cross-sensor correlative channel-aware fusion to achieve a final result. Furthermore, two datasets are selected to verify the effectiveness of the proposed method. The results demonstrate that our method is effective, robust, and suitable for diagnosis under limited data and complex conditions.

Modulation types clustering (MC) is crucial for adaptive high-frequency communication between devices in the Industrial Internet of Things. The strength of MC resides in its self-supervised framework, enabling it to extract modulation features efficiently without any manual labeling. However, the misalignment of proxy tasks and erroneous pseudolabeling constrain the performance of prevalent MC feature extraction techniques that utilize time series signals. In this article, we compare the saliency maps on time–frequency image (TFI) with that on time series signal, highlighting the consistency of TFI reconstruction with modulation feature extraction. Subsequently, in order to address the sensitivity of K-means to outliers, Sinkhorn–Knopp labeling (SKLb) is proposed to balance the scale of clusters and neighboring distances. Moreover, in consideration of the potential instability of the SKLb iteration result in backpropagation, the Sinkhorn–Knopp loss is proposed to ensure stable training of the model. Finally, two models, SK-IDC and SK-STDC, were tested on four datasets. Experimental results on these datasets present that our approach outperforms original signal representation and prevalent deep clustering methods, achieving State-of-the-Art performance.

Recently, contrastive learning (CL) has exhibited considerable advantages for automatic modulation classification (AMC) with a scarcity of labeled samples. Nevertheless, the majority of the available CL-based AMC methods use the simple signal augmentation strategy and suffer from interference from false negatives. To explore the more generalizable global temporal semantics within signals, a novel masked contrastive learning with hard negatives (MCLHN) method is proposed in this paper. MCLHN first strategically incorporates semantic-preserving data augmentation, ensuring the diversity and semantic invariance of signals. Second, MCLHN adopts an encoder with temporal masking to enable robust temporal modeling. Moreover, a debiased hardness-weighted contrastive (DHWC) loss is designed to balance the adverse impact of the debiased strategy and the advantage of hard negatives. Extensive experiments are conducted on several benchmark datasets, and the experimental results demonstrate the superior performance and generalization capability of MCLHN to other methods. Significantly, the performance of MCLHN with only one labeled sample per modulation under each signal-to-noise ratio (SNR) rivals that of other methods with five to twenty times the number of labeled samples.

Automatic modulation classification (AMC) is a crucial stage in the spectrum management, signal monitoring, and control of wireless communication systems. The accurate classification of the modulation format plays a vital role in the subsequent decoding of the transmitted data. End-to-end deep learning methods have been recently applied to AMC, outperforming traditional feature engineering techniques. However, AMC still has limitations in low signal-to-noise ratio (SNR) environments. To address the drawback, we propose a novel AMC-Net that improves recognition by denoising the input signal in the frequency domain while performing multi-scale and effective feature extraction. Experiments on two representative datasets demonstrate that our model performs better in efficiency and effectiveness than the most current methods.

Specific emitter identification (SEI) is crucial for attacking and defending Internet of Things (IoT) devices in untrusted scenarios or battlefield environments. However, existing SEI methods usually require annotation information, which is often unavailable in noncooperative communications and untrusted scenarios. In this article, we propose a signal contrastive self-supervised clustering (SCSC) method for unsupervised SEI applications. First, we propose SCSC with 1-D fingerprint pyramid feature extractor (1D-FPFE) for obtaining hierarchical subtle features of emitter signals. Then, we propose a bit-pulse selection (BPS) strategy and several signal data augmentation methods. By constructing signal positive and negative instance pairs through data augmentation, our approach generates cluster preference representations in a contrastive self-supervised learning manner. Extensive experimental results based on communication burst emitter dataset show that SCSC achieves an accuracy improvement of about 26% over the current best communication signal clustering algorithm. Moreover, SCSC also exhibits good performance and generalization for 30 emitter clustering and few-shot unlabeled signal clustering.

Internet of Things (IoT) networks are often subject to many malicious attacks in untrusted environments, and automatic modulation classification (AMC) is an effective way to combat IoT physical-layer threats. However, most existing AMC methods assume sufficient labeled signals and invariant signal distribution, which is often impossible in untrusted environments. In this article, a new meta-learning method is proposed for a few-shot AMC with distribution bias. First, a multi-frequency octave ResNet (MFOR) is constructed to learn coarse (low-frequency) and fine (high-frequency) features, which can efficiently identify the modulation type of the signal while saving computational resources. Second, a large number of classification-related meta-tasks are established for training MFOR to explore general knowledge in signal classification, and then transfer it to the AMC. Different with deep neural networks (DNNs) that learn a mapping by multiple instances, the MFOR with meta-learning (denoted as M-MFOR) can improve the generalization ability of new AMC tasks with very few instances and distribution bias. Furthermore, we find that the distribution bias between data can be reduced by adjusting the normalized distribution and propose a class-related mixup. Extensive experiments are taken on several datasets to investigate the effectiveness of M-MFOR. The results show its feasibility and superiority over existing methods.

Most existing classification methods design complicated and large deep neural network (DNN) model to deal with the ubiquitous spectral variability and nonlinearity of hyperspectral images (HSIs). However, their application is blocked by limited training samples and considerable computational costs in real scenes. To solve these problems, we propose a simple spectral hierarchical feature fusion and selection network (HFFSNet). Specifically, we apply 1-D grouped convolution for dimensionality reduction and multilevel feature extraction, then the multilevel features are fused to assist the adaptive feature selection of different layer features via the soft attention mechanism, and finally the selected features are fused to further enhance the feature representation. Extensive experimental results on three hyperspectral datasets demonstrate the effectiveness of the proposed network.

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.

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.

Supervised remote sensing (RS) image segmentation has achieved remarkable success with large amounts of manually labeled data, which may be difficult to acquire in some practical application scenarios. Semisupervised RS image segmentation can efficiently utilize the knowledge embedded in unlabeled data to improve recognition performance, which is of great significance for the generalization application of segmentation models. In this work, we propose an end-to-end semisupervised RS image segmentation method based on weak-to-strong consistency learning, denoted as WSCL. Specifically, a common strong data augmentation technique for image segmentation is introduced to provide powerful input perturbation to decouple self-biased cognition. By forcing weakly augmented, and strongly augmented perspectives from the same sample to be consistent, WSCL not only enables the model to steadily learn knowledge contained in unlabeled data but also alleviates overfitting. In addition, a novel sparse dual-view cross-sample image generation method is presented to generate new training samples, which helps provide a more comprehensive diversity of perturbations. Furthermore, an adaptive re-weighting strategy based on the entropy maps of the outputs of strongly perturbed samples is proposed to suppress noise, guiding the training process in a positive direction. Extensive experiments demonstrate the significant advantage of WSCL over other advanced methods, achieving new state-of-the-art under several evaluation metrics on DFC22, iSAID, MER, MSL, Vaihingen, and GID-15 datasets. The source code is open-sourced at https://github.com/xiaoqiang-lu/WSCL.

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 view of the good correlation measurement ability of correntropy, in this article, we propose a temporal local correntropy representation (TLCE) method based on the local correntropy matrix for fault diagnosis of machines. In TLCE, a sample is divided into several segments, and then, the correlation between these segments is expressed by correntropy. Finally, the correntropy matrix composed of the correntropy is regarded as the feature of each sample. The proposed TLCE model is validated by experiments of three bearing datasets and one gear dataset. And results demonstrate that compared with other methods, TLCE has obvious advantages, such as effectiveness and robustness.

In this paper, an Adaptive Fusion Transformer (AFT) is proposed for unsupervised pixel-level fusion of visible and infrared images. Different from the existing convolutional networks, transformer is adopted to model the relationship of multi-modality images and explore cross-modal interactions in AFT. The encoder of AFT uses a Multi-Head Self-attention (MSA) module and Feed Forward (FF) network for feature extraction. Then, a Multi-head Self-Fusion (MSF) module is designed for the adaptive perceptual fusion of the features. By sequentially stacking the MSF, MSA, and FF, a fusion decoder is constructed to gradually locate complementary features for recovering informative images. In addition, a structure-preserving loss is defined to enhance the visual quality of fused images. Extensive experiments are conducted on several datasets to compare our proposed AFT method with 21 popular approaches. The results show that AFT has state-of-the-art performance in both quantitative metrics and visual perception.

Autofocus plays a key role in synthetic aperture radar (SAR) imaging, especially for high-resolution imaging. In the literature, the minimum-entropy-based algorithms (MEA) have been proved to be robust and have been widely applied in SAR. However, this kind of method needs hundreds of iterations and is computationally expensive. In this paper, we proposed a non-iterative autofocus scheme based on deep learning and minimum-entropy criterion. It’s an unsupervised framework, which utilizes entropy as the loss function. In this scheme, deep neural networks are utilized for feature extraction and parameter estimation. Based on this scheme, two autofocus models (autofocus network and progressive autofocus network) are proposed. After training, the network learned the rules of autofocus from a large number of examples. Experimental results on real SAR data show that the proposed methods have focusing quality close to the state-of-the-art but with real-time focusing speed.

Semantic segmentation based on deep learning has achieved impressive results in recent years, but these results are supported by a large amount of labeled data which requires intensive annotation at the pixel level, particularly for high-resolution remote sensing (RS) images. In this work, we propose a simple yet efficient semisupervised learning framework based on linear sampling self-training, named LSST, to improve the performance of RS image semantic segmentation. Specifically, the classical pseudo-labeling-based self-training paradigm is enhanced by injecting strong data augmentations (SDA) applicable to RS images, based on which a powerful baseline is constructed. Nevertheless, the problem of insufficient data training to generate pseudo-labels with a high level of noise persists, and the noisy pseudo-labels will continue to accumulate and impede model improvement during the re-training phase. Previous works commonly employ a pre-defined threshold to remove noise, but it will lead to overfitting the model to easily identified classes. To address it, a method using linear sampling (LS) is presented for assigning thresholds to different classes in an adaptive manner, which provides noiseless regions for re-training. Experiments prove that the proposed pixel-wise selection is more available for segmentation than image-level selection in RS images. Finally, LSST achieves state-of-the-art on several datasets and different evaluation metrics. The source code of the this paper is available at https://github.com/xiaoqiang-lu/LSST.

The aim of multispectral (MS) and panchromatic (PAN) image fusion is to obtain an MS image that has high resolution in both spectral and spatial domains. During the fusion process, there are two important issues, i.e., spectral information preservation and spatial information enhancement. In this article, we propose a dual-collaborative fusion model that considers not only the spectral correlation collaboration but also the spatial-spectral collaboration. First, the features of PAN and MS images are extracted by a shared feature embedding network. Then, in order to enhance the spatial details, the PAN features are decomposed into four subbands, and the collaborative relationships among subbands are fully explored to refine the features. After the refinement of the subbands, the high-frequency components are directly taken as the inputs of the reconstruction network, while the low-frequency components are transformed by the guidance generation network to accomplish the spatial-spectral collaboration and also make preparations for the spectral adjustment. To explore the spectral correlation collaboration, a novel graph convolutional network is designed for the modulation of intraspectral relationships. Finally, the adjusted MS features are combined with the high-frequency components of PAN features to reconstruct the high-resolution MS image. Experimental results show that the proposed method outperforms traditional state-of-the-art pan-sharpening methods as well as the available deep learning-based ones.

Existing learned-based image compression methods have shown impressive performance. However, most of them rely on the consistency of distribution between training images and test images, which limits the robustness of the trained model. In this paper, we propose a novel compression method called sparse flow adversarial model (SFAM). SFAM employs a deep generative framework to learn a reversible and stable mapping between image distributions, thus it can work in varied scenes for robust compression. Moreover, a sparse adversarial map is introduced into SFAM, to constrain the SFAM to generate more sparser features for efficient compression. Extensive experiments are conducted on different datasets, in which the effectiveness and robustness of the proposed method is verified. Meanwhile, SFAM is trained only once and it can work well on three different datasets, which also proves the robustness of the proposed SFAM.

This paper proposes a Simple Semi-supervised Object Detection framework for Remote Sensing images, which is named SSOD-RS. SSOD-RS contains two parts, improved self-training and consistency regularization based on strong data augmentations with improved mixup. Firstly, as an augmentation algorithm, Object First mixup (OF-mixup) is proposed to adjust the weight of objects and the background, which expands the distribution of training samples while reducing the interference of the remote sensing complex background to the features of objects. Secondly, the strategy of training with assembling loss and fine-tuning is introduced into self-training to make the model fit the feature distribution of the true-labels after learning the features from the pseudo-labels. Experimental results demonstrate that SSOD-RS making use of unlabeled images can significantly improve the accuracy of the model.

Most of the available graph-based semisupervised hyperspectral image classification methods adopt the cluster assumption to construct a Laplacian regularizer. However, they sometimes fail due to the existence of mixed pixels whose recorded spectra are a combination of several materials. In this paper, we propose a geometric low-rank Laplacian regularized semisupervised classifier, by exploring both the global spectral geometric structure and local spatial geometric structure of hyperspectral data. A new geometric regularized Laplacian low-rank representation (GLapLRR)-based graph is developed to evaluate spectral-spatial affinity of mixed pixels. By revealing the global low-rank and local spatial structure of images via GLapLRR, the constructed graph has the characteristics of spatial-spectral geometry description, robustness, and low sparsity, from which a more accurate classification of mixed pixels can be achieved. The proposed method is experimentally evaluated on three real hyperspectral datasets, and the results show that the proposed method outperforms its counterparts, when only a small number of labeled instances are available.

In this paper a self-paced learning-based probability subspace projection (SL-PSP) method is proposed for hyperspectral image classification. First, a probability label is assigned for each pixel, and a risk is assigned for each labeled pixel. Then, two regularizers are developed from a self-paced maximum margin and a probability label graph, respectively. The first regularizer can increase the discriminant ability of features by gradually involving the most confident pixels into the projection to simultaneously push away heterogeneous neighbors and pull inhomogeneous neighbors. The second regularizer adopts a relaxed clustering assumption to make avail of unlabeled samples, thus accurately revealing the affinity between mixed pixels and achieving accurate classification with very few labeled samples. Several hyperspectral data sets are used to verify the effectiveness of SL-PSP, and the experimental results show that it can achieve the state-of-the-art results in terms of accuracy and stability.

Recognizing scenes from synthetic aperture radar (SAR) images has been a challenging task due to the increasing resolution of SAR data. Extracting discriminative features from SAR images is extremely difficult for their sensitivity to target aspect. Considering the intractability of the available deep neural networks in practical implementations, in this brief, we propose a simple and efficient deep sparse tensor filtering network (DSTFN) for SAR image classification. An SAR image is first organized into a data tensor by an overlapped partition. Then, a set of dimension-inseparable geometric filters is developed from a least squares support vector machine, followed by a learned sparse filtering of tensors. Finally, the constructed sparse tensor filters are cascaded to a deep network to automatically extract the discriminative features of the image for accurate classification. Simulations are carried out to verify the effectiveness of the proposed DSTFN.

Although the graph-based machine learning has received considerable attention in the remote sensing area and it has been widely used for terrain classification, the construction of graph in most existing algorithms still takes large memory and plenty of computational time especially for large Polarimetric Synthetic Aperture Radar (PolSAR) data. Addressing these issues, we propose a fast semi-supervised classification method based on parallel auction graph in this paper. The spatial relation between pixels is firstly preprocessed using the superpixel segmentation. Then we divide the PolSAR data into multiple groups, and each of them is used to construct a sparse auction graph. The semi-supervised classification is performed parallel on those graphs. Experimental results on simulated and real PolSAR data demonstrate its efficiency and effectiveness compared with existing methods.

The past few years have witnessed prosperity of spectral-spatial processing of hyperspectral images. In this letter, in order to determine the optimal projection subspace of spectrums, we define discriminate spectral-spatial margins (DSSMs) to reveal the local information of hyperspectral pixels and explore the global structures of both labeled and unlabeled data via low-rank representation (LRR). Heterogeneous and homogeneous spectral-spatial neighbors of hyperspectral pixels are used to define DSSMs. By maximizing the DSSM of hyperspectral data and casting an LRR manifold regularizer on finding better projection, both the local and global information of hyperspectral data can be well explored to determine more discriminative features. Some experiments are taken on several real hyperspectral data sets, and the results exhibit its efficiency and superiority to the counterparts, when only a small number of labeled samples are available.

This paper proposes a novel ship detection scheme in coastal regions for high-resolution synthetic aperture radar (SAR) imagery based on prior knowledge of the different properties presented by target and clutter. To begin with, image segmentation and land masking are applied to eliminate the areas that are unlikely to contain targets and get the index image which indicates the likely target positions. Ship detection is conducted only on these likely target positions using power ring algorithm (PR), which can avoid unnecessary and exhaustive searches. In the discrimination stage, two new features named number of 8 connected regions and average power of target areas are proposed and used to form a discriminative feature group. Unlike most discriminators, which are based on supervised learning, we use an unsupervised method based on K-means clustering to deal with the situations where there are few or no labeled samples. Experimental results show that the proposed scheme is fast in speed and can detect most of the targets while few false alarms occur.