In view of the practical value of open datasets of hyperspectral images (HSIs), HSI open-set classification (OSC) has attracted more and more attention. Existing HSI OSC methods are usually based on learning labeled samples to identify unknown classes. However, due to the complex high-dimensional characteristics of HSIs and the limited number of labeled samples, the recognition of unknown classes based only on limited labeled samples often has low and unstable accuracy. To address this problem, we propose a semi-supervised graph constraint dual classifier network (SSGCDCN) that can achieve efficient and stable OSC by learning unknown class features and relationships among samples. First, a dual classifier consisting of a multi-classifier and multiple binary classifiers is constructed, which has the ability to discover the unknown class samples by assigning and enabling pseudo-labels to participate in model training to achieve unknown class feature learning. Then, to improve the classification accuracy of both known and unknown classes, a homogeneous graph constraint is imposed on SSGCDCN to learn the relationship information among samples (including labeled and unlabeled samples). This constraint can bring the features of similar samples closer while pushing apart features of dissimilar samples. Experiments evaluated on three datasets demonstrate that the proposed method can obtain superior OSC performance than other state-of-the-art classification methods.

Hyperspectral image open-set classification has gained increasing attention due to its practical significance. However, existing approaches face two major challenges: (1) poor and unstable classification performance under limited labeled samples, and (2) the lack of end-to-end open-set classification frameworks. To address these issues, we propose an Incremental Multi-Task Contrastive Learning Network (IMTCLN), which integrates four learning tasks to achieve end-to-end open-set classification under few-shot conditions through feature sharing and multi-task collaboration. First, we introduce an expanded class labeling method in the model’s output layer, enabling end-to-end open-set classification. Second, among the four learning tasks, the supervised classification task learns the mapping between known-class samples and their labels using limited labeled data. To enhance classification performance under few-shot conditions, we design a semi-supervised Euclidean contrastive learning task, which improves intra-class compactness and inter-class separability by modeling homogeneous and heterogeneous sample relationships. Additionally, for effective unknown-class recognition, we propose a supervised Mahalanobis contrastive learning task, optimizing the Mahalanobis distance among known classes to identify unknown-class samples. Finally, to further enhance classification stability, we introduce an incremental learning task, which leverages pseudo-labeled unknown-class samples to learn their discriminative features, enabling robust discrimination between known and unknown classes. Extensive experiments on three public datasets demonstrate that IMTCLN significantly outperforms existing methods, particularly under extremely limited labeled samples, showcasing superior open-set classification performance and stability.

Infrared small-target detection (ISTD) in a single frame is an essential, yet challenging task due to its small size of targets, weak energy, and clutter background. Current methods either design complex network architectures to facilitate multilevel information interaction (e.g., DNA-Net and UIU-Net) or introduce structural texture priors to enhance feature discrimination (e.g., SRNet and CSRNet). However, both methods fail to explicitly distinguish or suppress the interference of complex background from infrared small targets, which makes them easy to “get lost” in clutter background with insufficient attention to the targets. In this work, we innovatively propose a novel background-masking approach (denoted as BGM) for ISTD. The proposed BGM aims to force the network to focus exclusively on the target by masking out irrelevant background information, thereby enhancing the network’s ability to detect weak and small infrared targets. Specifically, we present a new ISTD method that leverages a proxy training task with masking, enabling the network to simultaneously predict on both the original input and the masked data, where the background is randomly masked/forgotten. This strategy allows for a better concentration of the model on the shapeless targets rather than the cluttered background. The method is flexible with a simple U-shaped network without complicated manipulation and also computationally efficient without increasing the overall computational burden during inference. Extensive experiments demonstrate that our proposed BGM effectively enhances the detection performance of infrared small targets and achieves 70.8% mean intersection over union (mIoU) on IRSTD-1K. The source code would be available athttps://github.com/ZhihaoMa123/BGM

Existing methods for hyperspectral image classification (HSIC) typically assume a closed-set scenario, where all target types are known, and the classifier can assign only predefined classes to samples (pixels). However, in real remote sensing applications, open-set scenarios are common, where unknown classes exist. To address this problem, we propose a graph-constrained deep multi-task approach for open-set HSIC. Our method tackles the challenge of detecting unknown classes by integrating multiple-class classifiers and multiple binary classifiers. Additionally, to handle the limited labeled samples issue in HSIC, we propose utilizing homogeneous and heterogeneous graphs to constrain the two types of classifiers, thereby improving the accuracy of unknown class detection and known class classification. Experimental results on the Pavia University dataset demonstrate that our proposed method outperforms other closed-set and open-set classification methods significantly.

Nowadays, various types of foundational models have emerged, showcasing remarkable performance across a multitude of downstream tasks. However, in the domain of hyperspectral image classification (HSIC), substantial research is still required to effectively leverage the advantages of foundational models and adapt them to hyperspectral data. Consequently, we propose a HSIC algorithm based on a fixed-parameter SAM encoder. Specifically, the global feature extraction subnetwork integrates global patch information to obtain processed features. Subsequently, the semantic information extraction subnetwork is trained using cross-entropy to extract semantic features of categories, culminating in pixel-level classification. Experiments on two HSI datasets indicate that the proposed method can obtain better classification performance when compared with seven state-of-the-art methods.

Nowadays, foundation models have demonstrated exceptional performance across numerous downstream tasks. However, the effective application of these models to hyperspectral image classification (HSIC) is challenged by the unique characteristics of hyperspectral data, including high dimensionality, high variability, and high spatial structure complexity. Therefore, methods need to be developed, which leverage the advantages of foundation models while addressing these challenges. First, a novel HSIC algorithm based on a frozen-parameter segment anything model (SAM) encoder, called SAGFFNet, is proposed. This framework represents the first attempt to use a frozen SAM encoder for global feature extraction and to use spectral dimension data as prompts, enabling precise global spatial-spectral feature extraction with the aid of spectral information. Second, by introducing the self-adaptive padding mechanism and the global feature extraction subnetwork (GFEsNet), the model is enabled to extract distinctive and discriminative features for each category from hyperspectral data through varying padding sizes, thereby enhancing the feature extraction and generalization capabilities of the foundation model. Subsequently, the spectral feature prompt subnetwork (SFPsNet) is designed to extract spectral feature information from samples of different classes as prompt features, assisting the framework in better understanding the global features extracted by GFEsNet. Finally, the semantic information decoder subnetwork (SIDsNet) is introduced as a semantic information decoder, achieving efficient fusion of global spatial-spectral features and spectral prompt features, which significantly improves classification performance. Experiments conducted on four hyperspectral image datasets show that the proposed method outperforms nine existing approaches in terms of classification accuracy.

Hyperspectral remote sensing images exhibit fine spectral curves, but they are also susceptible to spectral variations caused by factors like cloud and haze. It is evident that these issues become more pronounced when there is a limited number of labeled samples available. Thus, a full range feature extraction network (FRFENet) based on quality-quantity-balance sample enhancement is proposed for hyperspectral image classification. First, the full-range feature extraction method combines local-range, short-range, and long-range spatial-spectral features to address spectral variability and ensure accurate feature extraction, particularly in scenarios with limited labeled samples. Furthermore, the approach of balancing quality and quantity for pseudo-labeled samples allows for an increased number of pseudo-labels while maintaining their quality, effectively leveraging unlabeled samples. Additionally, the utilization of superpixel region homogeneity directly contributes to an expanded training sample set, resulting in improved classification performance of the algorithm. Experiments on three HSI datasets indicate that the FRFENet can obtain better classification performance when compared with the other ten state-of-the-art methods.

Deep learning (DL) based methods represented by convolutional neural networks (CNNs) are widely used in hyperspectral image classification (HSIC). Some of these methods have strong ability to extract local information, but the extraction of long-range features is slightly inefficient, while others are just the opposite. For example, limited by the receptive fields, CNN is difficult to capture the contextual spectral-spatial features from a long-range spectral-spatial relationship. Besides, the success of DL-based methods is greatly attributed to numerous labeled samples, whose acquisition are time-consuming and cost-consuming. To resolve these problems, a hyperspectral classification framework based on multi-attention Transformer (MAT) and adaptive superpixel segmentation-based active learning (MAT-ASSAL) is proposed, which successfully achieves excellent classification performance, especially under the condition of small-size samples. Firstly, a multi-attention Transformer network is built for HSIC. Specifically, the self-attention module of Transformer is applied to model long-range contextual dependency between spectral-spatial embedding. Moreover, in order to capture local features, an outlook-attention module which can efficiently encode fine-level features and contexts into tokens is utilized to improve the correlation between the center spectral-spatial embedding and its surroundings. Secondly, aiming to train a excellent MAT model through limited labeled samples, a novel active learning (AL) based on superpixel segmentation is proposed to select important samples for MAT. Finally, to better integrate local spatial similarity into active learning, an adaptive superpixel (SP) segmentation algorithm, which can save SPs in uninformative regions and preserve edge details in complex regions, is employed to generate better local spatial constraints for AL. Quantitative and qualitative results indicate that the MAT-ASSAL outperforms seven state-of-the-art methods on three HSI datasets.

Nowadays, graph convolution networks are getting more and more attention in the field of hyperspectral image classification. The graph convolution can be divided into long-range and short-range graph convolution (GConv). However, the two graph convolutions cannot acquire global and local features at the same time, making the node features may not be accurate enough. Therefore, we propose a novel graph convolution approach, called short and long range graph convolution (SLGConv), which combines the advantages of long-range and short-range GConv. SLGConv can extract long-range (global) and short-range (local) spatial-spectral features, eliminating the disadvantages of each of long-range and short-range graph convolution. Furthermore, SLGConv can ensure that the features of nodes are not smoothed in the convolution process. Then, three layers of SLGConv are used to form the short and long range graph convolution network (SLGCN) for hyperspectral image classification. Experiments on three HSI datasets indicate that the SLGCN can obtain better classification performance when compared with seven state-of-the-art methods.

Convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification. However, a shape-fixed convolution kernel cannot extract appropriate spatial-spectral features. Thus, we propose a novel two-stage classification method based on kernel-guided deformable convolution networks and double-window joint bilateral filter (KDCDWBF) for HSIs. First, according to the calculated similarity map, the shape of the kernel-guided deformable convolution (KDC) is more consistent with the real shape of land covers, so the KDC can extract more pure neighborhood spatial-spectral information. Then, using the piecewise smoothness property of the HSI, a double-window joint bilateral filter (DWJBF) is designed to complete the coarse-to-fine classification stage, which can solve the misclassification problem of single pixels and small regions. Experiments on two HSI datasets demonstrate that the proposed network can achieve better classification performance when compared with other state-of-the-art methods.

Nowadays, domain adaptation (DA) is getting more attention in cross-scene hyperspectral image (HSI) classification, and various DA algorithms have been proposed. However, regular convolution indiscriminately extracting features around the center pixel will result in the inaccurate extraction of spatial-spectral features, which significantly affect the subsequent feature alignment. Meanwhile, the method of aligning the category features of source and target domains from a single-level may not cope well with complex HSIs. Therefore, we propose a multilevel feature alignment algorithm based on spatial attention deformable convolution (MFA-SADC), which achieves multilevel feature alignment from feature to feature, feature to cluster-center, and cluster-center to cluster-center. In addition, spatial attention deformable convolution is proposed to compose the feature extraction network of MFA-SADC, which guarantees the purity of spatial-spectral features. Experiments on three HSI datasets indicate MFA-SADC can obtain better classification performance when compared with the seven state-of-the-art methods.

Despite success in the same-scene hyperspectral image classification (HSIC), for the cross-scene classification, samples between source and target scenes are not drawn from the independent and identical distribution, resulting in significant performance degradation. To tackle this issue, a novel unsupervised domain adaptation (UDA) framework toward multilevel features and decision boundaries (ToMF-B) is proposed for the cross-scene HSIC, which can align task-related features and learn task-specific decision boundaries in parallel. Based on the maximum classifier discrepancy, a two-stage alignment scheme is proposed to bridge the interdomain gap and generate discriminative decision boundaries. In addition, to fully learn task-related and domain-confusing features, a convolutional neural network (CNN) and Transformer-based multilevel features extractor (generator) is developed to enrich the feature representation of two domains. Furthermore, to alleviate the harm even the negative transfer to UDA caused by task-irrelevant features, a task-oriented feature decomposition method is leveraged to enhance the task-related features while suppressing task-irrelevant features, and enabling the aligned domain-invariant features can be contributed to the classification task explicitly. Extensive experiments on three cross-scene HSI benchmarks have validated the effectiveness of the proposed framework.

Nowadays, graph convolution networks (GCNs) are getting more attention in hyperspectral image classification, and various algorithms based on GCNs have been proposed. However, because of hyperspectral images’ complex spatial texture information, the long-range graph convolution (GConv) and short-range GConv may cause inaccurate or over-smoothed feature extraction of some nodes. Thus, a multiscale short and long range graph convolution network (MSLGCN) is proposed for hyperspectral image classification. First, MSLGCN not only extracts spatial information of ground objects at different scales but also simultaneously captures global and local spectral features, which preserves objects’ fine boundaries. Then, the rich multiscale information is complementary, enabling the MSLGCN to take full advantage of texture structures of varying sizes. In addition, a method to determine the superpixel scale by the intrinsic properties of hyperspectral images is proposed to ensure that the segmentation boundary depicts the texture structure of the object accurately. Finally, the short-long graph convolution (SLGConv) is designed to fuse the advantages of global and local features, enabling the MSLGCN to extract accurate spatial-spectral features of nodes at any location. Experiments on three HSI datasets indicate that the MSLGCN can obtain better classification performance when compared with the other eleven state-of-the-art methods.

Convolutional neural networks are widely used in the field of hyperspectral image classification because of their excellent nonlinear feature extraction ability. However, as the sampling position of the regular convolution kernel is unchangeable, the regular convolution cannot distinctively extract the spatial and spectral information around the central pixel, which makes the classification results at the boundaries of ground objects over-smoothed and the classification performance degraded. Thus, we propose a novel superpixel guided deformable convolution network (SGDCN) for hyperspectral image classification. Firstly, the superpixel region fusion filter (SRF-Filter) is designed to fuse the initial superpixel region segmented by the simple linear iterative clustering (SLIC), making the fused superpixel region have a high homogeneity and also contain spatial features of diverse scales. Then, the superpixel guided deformable convolution (SGD-Conv) is proposed to make the shape of deformable convolution consistent with the real shape of land covers, and the SGD-Conv can extract pure neighborhood spatial-spectral features. Finally, a superpixel joint bilateral filter (SPJBF) is designed to solve the pixel-level and region-level misclassification problem, which can effectively utilize the superpixel region's homogeneity and improve the classification accuracy. Experiments on three HSI datasets indicate that the SGDCN can obtain better classification performance when compared with other twelve state-of-the-art methods.

This article is devoted to solving the problem of quality-related root cause diagnosis for nonlinear process. First, an orthogonal kernel principal component regression model is constructed to achieve orthogonal decomposition of feature space, such that quality-related and quality-unrelated faults can be separately detected in the subspaces of opposite correlations to the output, without any effect on each other. Then, in view of the high complexity of traditional nonlinear fault diagnosis methods, an efficient method of kernel sample equivalence replacement is established to replace the partial differential operations of the kernel gradient algorithm, which can convert nonlinear fault detection indicators into the standard quadratic forms of the original variable sample, thereby making it possible to solve the nonlinear fault diagnosis problem by linear manners. Furthermore, a transfer entropy algorithm is utilized to the new model to analyze the causality between the diagnosed candidate faulty variables to find out the accurate root cause of the fault. Finally, comparative studies between the latest result and the proposed one are carried out in the Tennessee Eastman process to verify the effectiveness and superiority of the new method.

Coupon is a powerful tool for promotional marketing and customer targeting. Improving customer conversion effects by optimally allocating coupons to the right customers, however, is a nontrivial task. In particular, offering coupons to all customers may not be cost effective while offering coupons to randomly selected customers may not lead to optimal effects. In this paper, we address challenges in precision coupon targeting with a customer triage framework which would ration coupons precisely to customers who will be the most promising commercial conversions. Specifically, first we use random user-experiments to quantify the coupon effects of our interest. Then, we model the observation from user-experiments to predict the expected coupon effects on all customers. With the predicted coupon effects, we develop a joint decision strategy for customer triage to target customers with the most influential coupons under budget constraints. The joint decision strategy can dynamically balance exploitation and exploration in the decision process spanning multiple decision periods. Implemented on a real-world online takeout service system, our results show significant improvements in comparison with alternative approaches.

Understanding the target audience for popular television series is valuable for online video platform to manage advertising sales, purchase video copyrights, and compete with other video service platforms. Existing studies in this domain generally focus on using data mining and machine learning techniques to recommend television series to individual users or predict the popularity of television series. Knowing only the popularity of television series may, however, limit our ability to answer more in-depth questions and develop more intelligent applications. In this paper, we develop a data-driven framework to model and predict audience competition patterns for popular online television series. Specifically, we first construct a sequence of dynamic competition networks of television series by mining the detailed viewership records. Then, we design the Dynamic Deep Network Factorization (DDNF), a hybrid modeling framework for predicting the future competition networks. Our framework adopts the deep neural network (DNN) and the knowledge-base (KB) embedding to incorporate static features, and integrates the Long Short-Term Memory (LSTM) network to learn dynamic features of the television series. Finally, extensive experiments on real-world data sets validate the effectiveness of our approach compared with state-of-the-art baselines in predicting the audience competition for existing and new television series.

Guaranteed display (GD) has been a successful model for display advertising. Existing solutions usually model GD services as a crowd-level supply allocation problem. This formulation, however, not only ignores user heterogeneity within crowds, but also makes it difficult to incorporate individual-level constraints. In this paper, we present an large-scale system for personalized delivery in GD advertising services. A unique contribution is to model the allocation problem at the individual level that accounts for user-ad interactions. Therefore, our system can conveniently incorporate complex constraints, such as the priority of GD contracts, the display frequency of ads, and the effectiveness of ad slots arrangement. Moreover, we develop a real-time pacing strategy to fulfill GD contracts with smooth ad delivery and optimized ad performance, such as cost-per-click (CPC) and cost-per-action (CPA). Our system can be parallelized to efficiently compute the delivery solution with billions decision variables. Using both offline evaluation and online A/B tests, we demonstrate that our solution is effective in terms of both accuracy and efficiency.

Node location information with high accuracy is very important in many applications of wireless sensor networks. In addition, the nodes are power-limited, hence, we need to save energy to guarantee the operations of network. These two metrics positioning accuracy and energy consumption should be balanced, mean that one should improve them both at the same time. In this paper, we model the energy consumption of nodes in the network by using the carrier-sense multiple-access/collision-avoidance (CSMA/CS) technique in combination with request-to-send (RTS)/clear-to-send (CTS) mechanism based IEEE 802.11 protocol to perform the transmission process of nodes. Otherwise, based on the mathematical model of direction-of-arrival (DoA) estimation error variance as a function of Received-Signal-Strength (RSS) we derive the Cramer-Rao lower bound (CRLB) of achievable accuracy of the target node that jointly utilizes difference-received-signal-strength (DRSS) and DoA measurements to estimate location as the objective function of positioning error. The Non-dominated Sorting Genetic Algorithm (NSGA-II) optimization algorithm can effectively find the Nash Equilibrium or Pareto optimal solutions of our dual objective optimization problem. The results show a high performance in both localization accuracy and energy consumption of the proposed algorithm.

In this paper, we give a closed form expression for the statistics of two independent exponential for two-hop links of vehicular networks. Then these statistics results help us to analyze the performance of two-hop vehicular communication networks with vehicle relay based on Amplify and Forward (AF) protocol over flat Rayleigh-fading channels. It is shown that the choice of gain of AF effects on bounds on the performance of these relay vehicular networks. And outage probability formula is obtained. Furthermore, we give out specifically a vehicle relay selection scheme. The "best" vehicle relay has been selected by satisfying specific criteria in respect of signal-to-noise ratio (SNR). Finally, simulation results display the difference between regeneration and non-regeneration vehicular systems. At low average SNR, AF relay vehicular networks have better performance. However, these two kinds of systems have similar outage probability at high average SNR.

We present optimization algorithm for relay (RLY) selection and, source (SRC) and RLY power allocations in a cooperative multicast millimeter-wave (mmWave) system with non-line-of-sight (non-LoS) sensing. We show that there is a significant benefit to the system outage performance by allocating powers for the SRC and the RLYs selected from the candidates availing of line-of-sight (LoS) paths. Specifically, we consider the joint optimization of RLY selection, and, SRC and RLY power allocations to minimize the largest outage probability among all the common-multicast group (co-MGroup) users. The joint optimization problem is non-convex and the complexity of finding the optimal solution is extremely high. Using the dual-step iterative optimization (DIO) algorithm, the joint problem is decomposed into a non-convex RLY selection, and, a convex SRC and RLY power allocation problem. By exploiting the search method and the Lagrange multipliers algorithm, we present efficient algorithms that yield the optimal solutions for both RLY selection (non-convex) and, SRC and RLY power allocation problems. The simulation results indicate significant improvements both on outage probability and power consumption performances over the traditional two-stage multicasting.

Data collection is a basic application of wireless sensor networks, where every sensor node collects data and then forwards data to the Sink node through a multi-hop path. In traditional TDMA scheduling, sensors consume extra energy because of the state transitions which occur numerous times in a TDMA frame. And the longer the length of a TDMA frame, the longer duration of a data collection and the larger of time latency. For the problem mentioned above, Slot Reuse Continuous Link Scheduling problem is proposed in this paper, and then formalized into a cross-layer optimization problem involving the network, medium access control, and physical layers with the objective of maximizing the energy efficiency. We transform the problem into two sub-problems: constructing a data collection tree; joint power control and link scheduling for the data collection tree. Based on an existing data collection tree, we propose an algorithm for allocating a continuous slot and transmission power on every link. Simulation results show that the proposed algorithm can shorten the duration of a data collection process, reduce the number of state transitions and energy consumption, improve network energy efficiency when compared with other existing data collection strategies.

The intensive downlink (DL) inter-cell interference created by cell range expansion (CRE) of picocells is an urgent problem needing to be solved under macro-pico heterogeneous network (HetNet) scenario. A promising approach is taking advantage of additional degree of freedom brought by carrier aggregation (CA). However, poorly arranged carrier configurations or interference coordination schemes can lead to a degradation of system overall performance. In this paper, we propose a novel dynamic interference coordination scheme based on carrier aggregation to alleviate the DL interference from macrocells to users located in the cell range expansion area. Novel carrier configuration pattern and dynamic power control scheme based on price algorithm are applied to mitigate detrimental interference to picocell-edge users and improve the availability of macrocells. Simulation results show that the proposed scheme can boost the picocell-edge throughput significantly while ameliorating the overall system throughput.