We introduce Conformal Interquantile Regression (CIR), a conformal regression method that efficiently constructs near-minimal prediction intervals with guaranteed coverage. CIR leverages black-box machine learning models to estimate outcome distributions through interquantile ranges, transforming these estimates into compact prediction intervals while achieving approximate conditional coverage. We further propose CIR+ (Conditional Interquantile Regression with More Comparison), which enhances CIR by incorporating a width-based selection rule for interquantile intervals. This refinement yields narrower prediction intervals while maintaining comparable coverage, though at the cost of slightly increased computational time. Both methods address key limitations of existing distributional conformal prediction approaches: they handle skewed distributions more effectively than Conformalized Quantile Regression, and they achieve substantially higher computational efficiency than Conformal Histogram Regression by eliminating the need for histogram construction. Extensive experiments on synthetic and real-world datasets demonstrate that our methods optimally balance predictive accuracy and computational efficiency compared to existing approaches.

We introduce Density-Sorted Prediction Set ( DSPS ), a novel method for uncertainty quantification in multi-target regression that uses conditional normalizing flows with conformal calibration. This approach constructs flexible, non-convex predictive regions with guaranteed coverage probabilities, overcoming limitations of traditional methods. By learning a transformation where the conditional distribution of responses follows a known form, DSPS identifies dense regions in the original space using the conditional probability density, which is computed via the Jacobian determinant and the latent density. This enables the creation of prediction regions that adapt to the true underlying distribution, focusing on areas of high probability density. Experimental results demonstrate that DSPS produces smaller, more informative prediction regions while maintaining robust coverage guarantees, enhancing uncertainty modeling in complex, high-dimensional settings.

Cybersecurity in safety-critical data communication infrastructures presents a significant and open challenge for cyber-physical systems (CPS). While extensive research exists on wireless jamming, effectively detecting attacks in noisy wireless environments remains difficult. This article introduces a novel framework for detecting mobile jamming attacks in LoRaWAN-based CPS, designed to operate robustly in the presence of communication faults. Unlike traditional methods that rely on physical-layer metrics like RSSI from end devices, our strategy uses only data upload statistics available at the backend network server. We propose a multi-stage filtering approach that first identifies anomalous upload failures and then distinguishes jamming attacks from communication faults by analyzing their distinct spatio-temporal signatures. Furthermore, the framework can reconstruct the attacker’s trajectory from the identified attack events. We evaluate the proposed strategy via extensive discrete-time simulations under various network densities and attacker speeds. Results demonstrate high efficacy, achieving an F1-score of up to 1.00 for event detection and a trajectory reconstruction mean absolute error (MAE) as low as 7.07 meters in dense networks, even in the presence of faults. This work’s primary contribution is a backend-centric, fault-tolerant detection methodology that enhances situational awareness without imposing additional overhead on resource-constrained end devices.

Uncertainty quantification is essential in decision-making, especially when joint distributions of random variables are involved. While conformal prediction provides distribution-free prediction sets with valid coverage guarantees, it traditionally focuses on single predictions. This paper introduces novel conformal prediction methods for estimating the sum or average of unknown labels over specific index sets. We develop conformal prediction intervals for single target to the prediction interval for sum of multiple targets. Under permutation invariant assumptions, we prove the validity of our proposed method. We also apply our algorithms on class average estimation and path cost prediction tasks, and we show that our method outperforms existing conformalized approaches as well as non-conformal approaches.

This paper introduces Conformal Thresholded Intervals (CTI), a novel conformal regression method that aims to produce the smallest possible prediction set with guaranteed coverage. Unlike existing methods that rely on nested conformal frameworks and full conditional distribution estimation, CTI estimates the conditional probability density for a new response to fall into each interquantile interval using off-the-shelf multi-output quantile regression. By leveraging the inverse relationship between interval length and probability density, CTI constructs prediction sets by thresholding the estimated conditional interquantile intervals based on their length. The optimal threshold is determined using a calibration set to ensure marginal coverage, effectively balancing the trade-off between prediction set size and coverage. CTI's approach is computationally efficient and avoids the complexity of estimating the full conditional distribution. The method is theoretically grounded, with provable guarantees for marginal coverage and achieving the smallest prediction size given by Neyman-Pearson . Extensive experimental results demonstrate that CTI achieves superior performance compared to state-of-the-art conformal regression methods across various datasets, consistently producing smaller prediction sets while maintaining the desired coverage level. The proposed method offers a simple yet effective solution for reliable uncertainty quantification in regression tasks, making it an attractive choice for practitioners seeking accurate and efficient conformal prediction.

Graph Neural Networks (GNNs) has been widely used in a variety of fields because of their great potential in representing graph-structured data. However, lacking of rigorous uncertainty estimations limits their application in high-stakes. Conformal Prediction (CP) can produce statistically guaranteed uncertainty estimates by using the classifier's probability estimates to obtain prediction sets, which contains the true class with a user-specified probability. In this paper, we propose a Rank-based CP during training framework to GNNs (RCP-GNN) for reliable uncertainty estimates to enhance the trustworthiness of GNNs in the node classification scenario. By exploiting rank information of the classifier's outcome, prediction sets with desired coverage rate can be efficiently constructed. The strategy of CP during training with differentiable rank-based conformity loss function is further explored to adapt prediction sets according to network topology information. In this way, the composition of prediction sets can be guided by the goal of jointly reducing inefficiency and probability estimation errors. Extensive experiments on several real-world datasets show that our model achieves any pre-defined target marginal coverage while significantly reducing the inefficiency compared with state-of-the-art methods.

Conformal prediction provides a robust framework for generating prediction sets with finite-sample coverage guarantees, independent of the underlying data distribution. However, existing methods typically rely on a single conformity score function, which can limit the efficiency and informativeness of the prediction sets. In this paper, we present a novel approach that enhances conformal prediction for multi-class classification by optimally averaging multiple conformity score functions. Our method involves assigning weights to different score functions and employing various data splitting strategies. Additionally, our approach bridges concepts from conformal prediction and model averaging, offering a more flexible and efficient tool for uncertainty quantification in classification tasks. We provide a comprehensive theoretical analysis grounded in Vapnik–Chervonenkis (VC) theory, establishing finite-sample coverage guarantees and demonstrating the efficiency of our method. Empirical evaluations on benchmark datasets show that our weighted averaging approach consistently outperforms single-score methods by producing smaller prediction sets without sacrificing coverage.

As deep learning models are increasingly deployed in high-risk applications, robust defenses against adversarial attacks and reliable performance guarantees become paramount. Moreover, accuracy alone does not provide sufficient assurance or reliable uncertainty estimates for these models. This study advances adversarial training by leveraging principles from Conformal Prediction. Specifically, we develop an adversarial attack method, termed OPSA (OPtimal Size Attack), designed to reduce the efficiency of conformal prediction at any significance level by maximizing model uncertainty without requiring coverage guarantees. Correspondingly, we introduce OPSA-AT (Adversarial Training), a defense strategy that integrates OPSA within a novel conformal training paradigm. Experimental evaluations demonstrate that our OPSA attack method induces greater uncertainty compared to baseline approaches for various defenses. Conversely, our OPSA-AT defensive model significantly enhances robustness not only against OPSA but also other adversarial attacks, and maintains reliable prediction. Our findings highlight the effectiveness of this integrated approach for developing trustworthy and resilient deep learning models for safety-critical domains. Our code is available at https://github.com/bjbbbb/Enhancing-Adversarial-Robustness-with-Conformal-Prediction.

Graph Neural Networks (GNNs) excel at modeling relational data but face significant challenges in high-stakes domains due to unquantified uncertainty. Conformal prediction (CP) offers statistical coverage guarantees, but existing methods often produce overly conservative prediction intervals that fail to account for graph heteroscedasticity and structural biases. While residual reweighting CP variants address some of these limitations, they neglect graph topology, cluster-specific uncertainties, and risk data leakage by reusing training sets. To address these issues, we propose Residual Reweighted GNN (RR-GNN), a framework designed to generate minimal prediction sets with provable marginal coverage guarantees. RR-GNN introduces three major innovations to enhance prediction performance. First, it employs Graph-Structured Mondrian CP to partition nodes or edges into communities based on topological features, ensuring cluster-conditional coverage that reflects heterogeneity. Second, it uses Residual-Adaptive Nonconformity Scores by training a secondary GNN on a held-out calibration set to estimate task-specific residuals, dynamically adjusting prediction intervals according to node or edge uncertainty. Third, it adopts a Cross-Training Protocol, which alternates the optimization of the primary GNN and the residual predictor to prevent information leakage while maintaining graph dependencies. We validate RR-GNN on 15 real-world graphs across diverse tasks, including node classification, regression, and edge weight prediction. Compared to CP baselines, RR-GNN achieves improved efficiency over state-of-the-art methods, with no loss of coverage.

Abstract This paper investigates the application of probabilistic prediction methodologies in route planning within a road network context. Specifically, we introduce the Conformalized Quantile Regression for Graph Autoencoders (CQR-GAE), which leverages the conformal prediction technique to offer a coverage guarantee, thus improving the reliability and robustness of our predictions. By incorporating uncertainty sets derived from CQR-GAE, we substantially improve the decision-making process in route planning under a robust optimization framework. We demonstrate the effectiveness of our approach by applying the CQR-GAE model to a real-world traffic scenario. The results indicate that our model significantly outperforms baseline methods, offering a promising avenue for advancing intelligent transportation systems.

Let $\mathbf{A} = \mathbf{L}_0 + \mathbf{S}_0$, where $\mathbf{L}_0 \in \mathbb{R}^{d\times d}$ is low rank and $\mathbf{S}_0$ is a perturbation matrix. We study the principal subspace estimation of $\mathbf{L}_0$ through observations $\mathbf{y}_j = f(\mathbf{A})\mathbf{x}_j$, $j=1,…,n$, where $f:\mathbb{R}\rightarrow \mathbb{R}$ is an unknown polynomial and $\mathbf{x}_j$’s are i.i.d. random input signals. Such models are widely used in graph signal processing to model information diffusion dynamics over networks with applications in network topology inference and data analysis. We develop an estimation procedure based on nuclear norm penalization, and establish upper bounds on the principal subspace estimation error when $\mathbf{A}$ is the adjacency matrix of a random graph generated by $\mathbf{L}_0$. Our theory shows that when the signal strength is strong enough, the exact rank of $\mathbf{L}_0$ can be recovered. By applying our results to blind community detection, we show that consistency of spectral clustering can be achieved for some popular stochastic block models. Together with the experimental results, our theory show that there is a fundamental limit of using the principal components obtained from diffused graph signals which is commonly adapted in current practice. Finally, under some structured perturbation $\mathbf{S}_0$, we build the connection between this model with spiked covariance model and develop a new estimation procedure. We show that such estimators can be optimal under the minimax paradigm.

Efficient inner product search on embedding vectors is often the vital stage for online ranking services, such as recommendation and information retrieval. Recommendation algorithms, e.g., matrix factorization, typically produce latent vectors to represent users or items. The recommendation services are conducted by retrieving the most relevant item vectors given the user vector, where the relevance is often defined by inner product. Therefore, developing efficient recommender systems often requires solving the so-called maximum inner product search (MIPS) problem. In the past decade, there have been many studies on efficient MIPS algorithms. This task is challenging in part because the inner product does not follow the triangle inequality of metric space.

We study the theory of random bipartite graph whose adjacency matrix is generated according to a connectivity matrix $M$. We consider the bipartite graph to be sparse, i.e., the entries of $M$ are upper bounded by certain sparsity parameter. We show that the performance of estimating the connectivity matrix $M$ depends on the sparsity of the graph. We focus on two measurement of performance of estimation: the error of estimating $M$ and the error of estimating the column space of $M$. In the first case, we consider the operator norm and Frobenius norm of the difference between the estimation and the true connectivity matrix. In the second case, the performance will be measured by the difference between the estimated projection matrix and the true projection matrix in operator norm and Frobenius norm. We will show that the estimators we propose achieve the minimax optimal rate.

In this letter, a novel convolutional neural network (CNN)-based dictionary learning (DL) method is proposed for synthetic aperture radar (SAR) target recognition. Different from conventional target recognition schemes, which consist of the hand-crafted feature extraction followed by a classifier, the proposed scheme utilizes a well-designed ConvNet as the feature extractor, and it can automatically learn hierarchies of features from the training data set. The outputs of the ConvNet are regarded as multifeature and are used for the following multi-DL. For a classification task, we take into consideration the mean-squared error (MSE) combined with a regularization term as the loss function. As a result, the whole architecture combines the ConvNet and DL as an end-to-end framework. We show the back propagation of the loss and update the variables using the stochastic gradient descent with the momentum method. Experiments performed on the moving and stationary target automatic recognition (MSTAR) data set exhibit that the proposed method outperforms many state-of-the-art DL and CNN methods in terms of recognition performance.

Matrix and tensor completion aim to recover the incomplete two- and higher-dimensional observations using the low-rank property. Conventional techniques usually minimize the convex surrogate of rank (such as the nuclear norm), which, however, leads to the suboptimal solution for the low-rank recovery. In this paper, we propose a new definition of matrix/tensor logarithmic norm to induce a sparsity-driven surrogate for rank. More importantly, the factor matrix/tensor norm surrogate theorems are derived, which are capable of factoring the norm of large-scale matrix/tensor into those of small-scale matrices/tensors equivalently. Based upon surrogate theorems, we propose two new algorithms called Logarithmic norm Regularized Matrix Factorization (LRMF) and Logarithmic norm Regularized Tensor Factorization (LRTF). These two algorithms incorporate the logarithmic norm regularization with the matrix/tensor factorization and hence achieve more accurate low-rank approximation and high computational efficiency. The resulting optimization problems are solved using the framework of alternating minimization with the proof of convergence. Simulation results on both synthetic and real-world data demonstrate the superior performance of the proposed LRMF and LRTF algorithms over the state-of-the-art algorithms in terms of accuracy and efficiency.

Recently, plenty of neural network based recommendation models have demonstrated their strength in modeling complicated relationships between heterogeneous objects (i.e., users and items). However, the applications of these fine trained recommendation models are limited to the off-line manner or the re-ranking procedure (on a pre-filtered small subset of items), due to their time-consuming computations. Fast item ranking under learned neural network based ranking measures is largely still an open question.

The thriving of deep models and generative models provides approaches to model high dimensional distributions. Generative adversarial networks (GANs) can approximate data distributions and generate data samples from the learned data manifolds as well. In this paper, we propose an approach to estimate the implicit likelihoods of GAN models. A stable inverse function of the generator can be learned with the help of a variance network of the generator. The local variance of the sample distribution can be approximated by the normalized distance in the latent space. Simulation studies and likelihood testing on real-world data sets validate the proposed algorithm, which outperforms several baseline methods in these tasks. The proposed method has been further applied to anomaly detection. Experiments show that the method can achieve state-of-the-art anomaly detection performance on real-world data sets.

We study bipartite community detection in networks, or more generally the network biclustering problem. We present a fast two-stage procedure based on spectral initialization followed by the application of a pseudo-likelihood classifier twice. Under mild regularity conditions, we establish the weak consistency of the procedure (i.e., the convergence of the misclassification rate to zero) under a general bipartite stochastic block model. We show that the procedure is optimal in the sense that it achieves the optimal convergence rate that is achievable by a biclustering oracle, adaptively over the whole class, up to constants. This is further formalized by deriving a minimax lower bound over a class of biclustering problems. The optimal rate we obtain sharpens some of the existing results and generalizes others to a wide regime of average degree growth, from sparse networks with average degrees growing arbitrarily slowly to fairly dense networks with average degrees of order $\sqrt{n}$. As a special case, we recover the known exact recovery threshold in the $\log n$ regime of sparsity. To obtain the consistency result, as part of the provable version of the algorithm, we introduce a sub-block partitioning scheme that is also computationally attractive, allowing for distributed implementation of the algorithm without sacrificing optimality. The provable algorithm is derived from a general class of pseudo-likelihood biclustering algorithms that employ simple EM type updates. We show the effectiveness of this general class by numerical simulations.

With the development of synthetic aperture radar (SAR) system, automatic target recognition (ATR) has attracted wide attention in many decision-making tasks, in which an enhanced feature of SAR image is a powerful tool to improve the recognition accuracy. However, the presence of speckle noise and natural clutter inevitably contaminates SAR images and, thus, degrades image features. In this article, we explicitly address the speckle reduction problem for the circular SAR system, in which the motion of aircraft platform causes continuous angular variations so that different SAR images can be captured with the high interrelationship. By exploiting the underlying low-rank and continuous properties among different SAR images, a method called the ℓp-regularized low-rank and space-angle continuity extraction (ℓp-LSCE) is proposed to suppress the noise and enhance the target feature. Taking into account the interrelationship between SAR images, we arrange the images in a 3-D tensor to investigate the space-angle continuity of the targets. Furthermore, we develop a robust ℓp-regularized scheme to incorporate the low-rank property of targets. Then, the joint optimization problem is solved via the framework of augmented Lagrange multiplier (ALM) with efficient computation of each ALM subproblem. The experimental results of circular SAR data sets of the moving and stationary target acquisition and recognition (MSTAR) and the VideoSAR demonstrate that the proposed method can efficiently despeckle SAR images with well-preserved target features, which is conducive to the improvement of ATR performance.

In this article, a supervised nonlinear dictionary learning (DL) method, called multiscale supervised kernel DL (MSK-DL), is proposed for target recognition in synthetic aperture radar (SAR) images. We use Frost filters with different parameters to extract an SAR image's multiscale features for data augmentation and noise suppression. In order to reduce the computation cost, the dimension of each scale feature is reduced by principal component analysis (PCA). Instead of the widely used linear DL, we learn multiple nonlinear dictionaries to capture the nonlinear structure of data by introducing the dimension-reduced features into the nonlinear reconstruction error terms. A classification model, which is defined as a discriminative classification error term, is learned simultaneously. Hence, the objective function contains the nonlinear reconstruction error terms and a classification error term. Two optimization algorithms, called multiscale supervised kernel K-singular value decomposition (MSK-KSVD) and multiscale supervised incremental kernel DL (MSIK-DL), are proposed to compute the multidictionary and the classifier. Experiments on the moving and stationary target automatic recognition (MSTAR) data set are performed to evaluate the effectiveness of the two proposed algorithms. And the experimental results demonstrate that the proposed scheme outperforms some representative common machine learning strategies, state-of-the-art convolutional neural network (CNN) models and some representative DL methods, especially in terms of its robustness against training set size and noise.

Tensor robust principal component analysis via tensor nuclear norm (TNN) minimization has been recently proposed to recover the low-rank tensor corrupted with sparse noise/outliers. TNN is demonstrated to be a convex surrogate of rank. However, it tends to over-penalize large singular values and thus usually results in biased solutions. To handle this issue, we propose a new definition of tensor logarithmic norm (TLN) as the nonconvex surrogate of rank, which can decrease the penalization on larger singular values and increase that on smaller ones simultaneously to preserve the low-rank structure of a tensor. Then, the strategy of tensor factorization is combined into the minimization of TLN to improve computational performance. To handle impulsive scenarios, we propose a nonconvex 'p-ball projection scheme with 0 < p < 1 instead of the conventional convex scheme with p = 1, which enhances the robustness against outliers. By incorporating the TLN minimization and the 'p-ball projection, we finally propose two low-rank recovery algorithms, whose resulting optimization problems are efficiently solved by the alternating direction method of multipliers (ADMM) with convergence guarantees. The proposed algorithms are applied to the synthetic data recovery and image and video restorations in real-world. Experimental results demonstrate the superior performance of the proposed methods over several state-ofthe- art algorithms in terms of tensor recovery accuracy and computational efficiency.

Shulong Tan, Zhixin Zhou, Zhaozhuo Xu, Ping Li. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

This paper proposes a method that can enhance the features of synthetic aperture radar images based on the exploitation of intrinsic target structure to improve the performance of automatic target recognition (ATR). We take advantage of the interrelationship between images and arrange them into a three-dimensional tensor. Then, by incorporating the joint low-rank and continuity constraints, the intrinsic target structure is extracted and enhanced with the reasonable suppression of speckle noise. Experiments on the moving and stationary target acquisition and recognition public database demonstrate the high quality of feature enhancement of the proposed algorithm, which efficiently improves the ATR performance.

We present a fast search on graph algorithm for Maximum Inner Product Search (MIPS). This optimization problem is challenging since traditional Approximate Nearest Neighbor (ANN) search methods may not perform efficiently in the non-metric similarity measure. Our proposed method is based on the property that Möbius transformation introduces an isomorphism between a subgraph of l^2-Delaunay graph and Delaunay graph for inner product. Under this observation, we propose a simple but novel graph indexing and searching algorithm to find the optimal solution with the largest inner product with the query. Experiments show our approach leads to significant improvements compared to existing methods.

We consider spectral clustering algorithms for community detection under a general bipartite stochastic block model (SBM). A modern spectral clustering algorithm consists of three steps: (1) regularization of an appropriate adjacency or Laplacian matrix (2) a form of spectral truncation and (3) a kmeans type algorithm in the reduced spectral domain. We focus on the adjacency-based spectral clustering and for the first step, propose a new data-driven regularization that can restore the concentration of the adjacency matrix even for the sparse networks. This result is based on recent work on regularization of random binary matrices, but avoids using unknown population level parameters, and instead estimates the necessary quantities from the data. We also propose and study a novel variation of the spectral truncation step and show how this variation changes the nature of the misclassification rate in a general SBM. We then show how the consistency results can be extended to models beyond SBMs, such as inhomogeneous random graph models with approximate clusters, including a graphon clustering problem, as well as general sub-Gaussian biclustering. A theme of the paper is providing a better understanding of the analysis of spectral methods for community detection and establishing consistency results, under fairly general clustering models and for a wide regime of degree growths, including sparse cases where the average expected degree grows arbitrarily slowly.

In this letter, a novel nonlinear supervised dictionary learning (DL) scheme called multiscale incremental DL, whose objective function contains reconstruction error terms and a classification error term, is proposed for synthetic aperture radar (SAR) object recognition. In the reconstruction error terms, considering the local and global features of SAR images, Gaussian functions with different blurring parameters are exploited to extract SAR images' multiscale features, and all features can be reconstructed according to the weights assigned to these features at different scales. In the classification error term, a linear combination of classification vectors close to the labels of samples restricts sparse codes from different classes to be almost independent. Furthermore, an incremental method is utilized to address the memory consumption problem, and the optimal solution is obtained. Experiments on the moving and stationary target automatic recognition database demonstrate that the proposed algorithm outperforms several representative DL, support vector machine, and k-nearest neighbor methods in the case of a small training sample set size and exhibits strong antinoise performance.

Due to the complexity of very high resolution (VHR) images and the inaccurate correspondence, change feature extraction is the key difficulty of VHR image change detection. In this letter, change field is proposed to represent the complex changes between VHR images. Change field measures the complex changes based on the displacements and the compensated distance. Based on change field, a novel change detection approach is proposed, where the inter-class variability is improved and the changed class and the unchanged class can be separated effectively. Experiments demonstrate the effectiveness of the proposed approach.

This letter presents a hierarchical semantic multi-instance multilabel learning (MIML) framework for high-resolution (HR) remote sensing image annotation via Gaussian process (GP). The proposed framework can not only represent the ambiguities between image contents and semantic labels but also model the hierarchical semantic relationships contained in HR remote sensing images. Moreover, it is flexible to incorporate prior knowledge in HR images into the GP framework which gives a quantitative interpretation of the MIML prediction problem in turn. Experiments carried out on a real HR remote sensing image data set validate that the proposed approach compares favorably to the state-of-the-art MIML methods.

In this letter, we propose a semisupervised context-sensitive technique for change detection in high-resolution multitemporal remote sensing images. This is achieved by analyzing the posterior probability of probabilistic Gaussian process (GP) classifier within a Markov random field (MRF) model. In particular, the method consists of two steps: 1) A semisupervised initialization exploits both labeled and unlabeled data based on a probabilistic GP classifier, and 2) an MRF regularization aims at refining the posterior probability by employing the spatial context information. In particular, both edge information and high-order potential are utilized in MRF energy function formulation. Experimental results obtained on real remote sensing multitemporal imagery data sets confirm the effectiveness of the proposed approach.

A fast approach is proposed in this letter for large-size very high resolution image registration, which is accomplished based on coarse-to-fine strategy and blockwise scale-invariant feature transform (SIFT) matching. Coarse registration is implemented at low resolution level, which provides a geometric constraint. The constraint makes the blockwise SIFT matching possible and is helpful for getting more matched keypoints at the latter refined procedure. Refined registration is achieved by blockwise SIFT matching and global optimization on the whole matched keypoints based on iterative reweighted least squares. To improve the efficiency, blockwise SIFT matching is implemented in a parallel manner. Experiments demonstrate the effectiveness of the proposed approach.

Vector Quantization (VQ) is widely used for Hyper Spectral Image (HSI) compression and VQ based algorithms yield good results for reducing the amount of the data. However, the VQ based algorithms have the shortcoming of computing expensive. Many fast VQ algorithms have been proposed to reduce the computing complexity, while the algorithms consider the HSI feature rarely. We present a new framework of fast vector quantization for HSI compression, which uses the spectra characteristics of HSI adequately. The breakthrough codebook training method is calculated at the HSI feature domain, which is a low dimension structure without losing significant information, to get much lower complexity. The experimental results demonstrate that the proposed algorithms can reduce the computing time dramatically while keep the comparable reconstruction fidelity.

Inspired by the idea of co-training algorithm, in this paper we propose a novel semi-supervised learning algorithm, co-Gaussian Process (co-GP), under a Bayesian framework. Image data are characterized in two distinct views, i.e. two disjoint feature sets. A latent function with a GP prior is employed for each view. In learning process of co-GP, knowledge acquired in each view is transferred by probabilistic labels to the other in turns to enhance learning effect. In this manner, proper parameters are estimated in a bootstrap mode and a satisfying performance can be maintained with only small amount of labeled data. The experiments carried out on multitemporal images validate the proposed algorithm.

A novel approach is presented for change detection of very high resolution images, which is accomplished by fast object-level change feature extraction and progressive change feature classification. Object-level change feature is helpful for improving the discriminability between the changed class and the unchanged class. Progressive change feature classification helps improve the accuracy and the degree of automation, which is implemented by dynamically adjusting the training samples and gradually tuning the separating hyperplane. Experiments demonstrate the effectiveness of the proposed approach.

This paper introduces a semi-supervised change detection method that exploits both labeled and unlabeled samples via Gaussian Process (GP). The proposed method is based on recent development in Gaussian Process classifier named NCNM [3]. NCNM is a probabilistic approach to learning a GP classifier in the presence of unlabeled data. It involves a novel transductive learning under a probabilistic framework. Experimental results obtained on two sets of multitemporal remote sensing images confirm the effectiveness of the proposed approach. It also proves that NCNM can compete seriously with the state-of-the-art support vector machines (SVM) classifier for remote sensing image change detection.

In this paper, we present a variational Bayesian (VB) approach to multitemporal remote sensing image change detection. The content of the so called `difference image' is modeled by finite Gaussians Mixture Model (GMM), then with the factor analysis techniques, underlying structure of image content is inferred automatically. Compared with the Expectation-Maximization (EM) algorithm, the proposed method can adaptively determine the number of components in the mixture model without usual sub- or over-segmentation problem. Moreover, to overcome the local optimization problem, a component split strategy is employed in inference process. Experimental results confirm the effectiveness of the proposed method.

Inspired by the idea of co-training algorithm, in this paper we propose a novel remote sensing image segmentation approach using co-training strategy under variational Bayesian (VB) framework. Image data are characterized in two distinct views, i.e. two disjoint feature sets. A Gaussian mixture model (GMM) is employed for each view. On one hand, underlying structure of image content is inferred automatically with the factor analysis techniques. On the other hand, parameters are estimated in a bootstrap mode with the co-training strategy. In this manner, a satisfying performance can be achieved. Experimental analyses carried out on several different sets of high resolution optical images validate the proposed algorithm.

The judgements in some sports competitions are subjective tasks, especially in competitions with high requirements on skills, which could lead to unfair results. Synchronized diving is such a skillful competition. Using computer to judge competitions automatically or assist referees to judge can effectively decrease the unfair results. This paper presents a framework for analyzing synchronization for synchronized diving videos. The framework is composed of three steps: (1) silhouette extraction, that can effectively get the diverpsilas silhouette; (2) synchronization feature extraction and representation, that can explicitly achieve synchronization features from synchronized diving videos; (3) synchronization evaluation, that can evaluate synchronization with evaluation function which is constructed by statistic learning method with diving video segments come from different games judged by different referees. The experiment shows that the method can accurately and effectively evaluate the synchronization for synchronized diving videos.

Usually changes in remote sensing images go along with the appearance or disappearance of some edges. In addition, pixels located along the edges are likely to weakly influenced by its neighborhood pixels, while pixels located far from the edges commonly have a tightly correlation among them. In this paper, we propose a novel change detection technique based on adaptive Markov Random Fields (MRFs) for high resolution satellite images with combined color and texture features. The technique is composed of two main steps: (1) the input images are marked with different region indexes by the combined color and edge features; (2) change maps are obtained under the MRF framework with alterable order of neighborhood and variable smooth weight coefficient controlled by the index map. The main contribution of this paper is that the spatial-contextual information included in the remote sensing imagery is correctly and adaptively exploited under an adaptive MRF framework. Experiments results obtained on a set of remote sensing imagery confirm the effectiveness of the proposed approach.

In this paper, we present an unsupervised change detection approach in temporal sets of SAR images. The change detection is represented as a task of energy minimization and the energy function is minimized using graph cuts. Neighboring pixels are taken into account in a priority sequence according to their distance from the center pixel, and the energy function is formed based on Markov Random Field (MRF) model. Graph cuts algorithm is employed for computing maximum a-posteriori (MAP) estimates of the MRF. Experiments results obtained on a SAR data set confirm the effectiveness of the proposed approach. The comparisons between graph cuts algorithm and iterated conditional modes (ICM) algorithm about the quality of change map and running time of energy minimization illustrate that graph cuts algorithm is a huge improvement over ICM.

A multilevel contextual approach is proposed in this paper for change detection of VHR images. By representing the change features in a hierarchical contextual manner, the changes are detected level-by-level. By taking advantages of SVMs, the ambiguity of changes is mitigated and the optimal changes are detected peculiar to the specific user. Compared to the traditional methods, the proposed approach is more accurate, more robust and faster. Experiments demonstrate the effectiveness and advantages of the proposed approach.

A multiscale approach is presented in this paper for urban change detection of VHR images. The proposed approach detects the changes at different scales by local-region-based approach, which consists of local region extraction, local region description and local region comparison. To combine the changes at different scales and improve the accuracy, multiscale fusion strategy is applied to local-region-based change detection. Experimental results obtained on Quickbird images confirm the effectiveness of the proposed approach.

This paper introduces an interferometric imaging altimeter system designed for both ocean and land observations. This sensor combines the functions of interferometric radar altimeter and SAR together and is aimed to provide centimeter- level accuracy of topography for ocean and meter-level accuracy of topography for land. With the interferometry technique, a wide swath of 80 km is achieved, and with the synthetic aperture technique, a resolution of 100 m for land observation is achieved. System design is outlined and some preliminary results are presented.

Because of active and side-look imaging and speckles in the SAR image, the road edge is blur and it is difficult to determinate the road edge. Therefore, extraction of the road from high-resolution SAR image can't use the methods that were used to the optical image. In this paper, we research how to extract the information of zonal road and how to get the vector road from the high-resolution SAR image. Usually, urban road is regular. In remote sensing image, the road has many characters, such as functional character, spectral character, geometric character and texture character so on. These characters consist of the knowledge of road extraction. After accurately expressing the character knowledge with mathematical equations, we can extract the road information more accurately. Therefore, we must construct road model after analyzing the road characters in the high-resolution SAR image. Based on the above analysis, we may accurately extract road from SAR image. By our experiment, the result proves that our method is a good idea.

Motion correction is required in airborne synthetic aperture radar to generate high quality images. The paper proposes a new motion compensation method based on subaperture techniques which better approximates the space variant of the compensation kernel. Motion errors are averaged and added to the system parameters. Only residual errors remain to be corrected. The subaperture images are compensated independently and added together coherently in the final step to generate a fine resolution image. Simulation results show that the new method outperforms full aperture compensation methods, especially in the case of large motion errors.