In recent years, deep learning (DL) has achieved remarkable success in classifying hyperspectral images (HSIs), relying heavily on the quantity and quality of labeled samples. However, obtaining sufficient labels for HSIs poses a challenge. HSIs obtained by the same sensor often exhibit similar spectral information due to their shared physical, chemical properties, or reflective attributes. Joint analysis of several HSIs enables the integration of limited labeled samples and extraction of more robust and discriminative features from different HSIs. Therefore, a multitask collaborative network (MTCN) for the joint classification of multiple HSIs acquired by the same sensor in different areas is proposed. In the MTCN, each HSI has its own feature extraction channel, which facilitates the learning of image-specific representations. In addition, a feature sharing channel (FSC) is created to extract and transfer multihierarchical image-shared representations between multiple HSIs, thereby forming a common knowledge pool to facilitate feature sharing. Furthermore, a cross-channel mutual attention module (CMAM) is designed to collaboratively utilize features from image-specific and image-shared channels, enhancing the efficiency of information communication in HSIs. The experimental results on six HSIs demonstrate that the proposed MTCN can jointly classify multiple HSIs by the same sensor in different areas and achieve good classification performance.

In the absence of sufficient labels, deep neural networks (DNNs) are prone to overfitting, resulting in poor performance and difficulty in training. Thus, many semisupervised methods aim to use unlabeled sample information to compensate for the lack of label quantity. However, as the available pseudolabels increase, the fixed structure of traditional models has difficulty in matching them, limiting their effectiveness. Therefore, a deep-growing neural network with manifold constraints (DGNN-MC) is proposed. It can deepen the corresponding network structure with the expansion of a high-quality pseudolabel pool and preserve the local structure between the original and high-dimensional data in semisupervised learning. First, the framework filters the output of the shallow network to obtain pseudolabeled samples with high confidence and adds them to the original training set to form a new pseudolabeled training set. Second, according to the size of the new training set, it increases the depth of the layers to obtain a deeper network and conducts the training. Finally, it obtains new pseudolabeled samples and deepens the layers again until the network growth is completed. The growing model proposed in this article can be applied to other multilayer networks, as their depth can be transformed. Taking HSI classification as an example, a natural semisupervised problem, the experimental results demonstrate the superiority and effectiveness of our method, which can mine more reliable information for better utilization and fully balance the growing amount of labeled data and network learning ability.

Novel benchmarks for multiobjective neural archi-tecture search are emerging consistently. It stimulates the need of knowledge transfer techniques to facilitate the repetitive and cumbersome search process. One-shot surrogate is hereby proposed to transfer knowledge from source problems. Specif-ically, a Pareto-aware super-surrogate construction technique is proposed aiming at efficient exploitation of knowledge from benchmarks to make transfer easier. Then, during the surrogate prediction process, appropriate sub-surrogates are sampled from the super-surrogate to jointly guide the evaluation decision. The multiobjective surrogate is also redesigned as objective-wise, decision-making, and combinative ones, so that it can make transfer flexible, as well as evade from the scalarization issue in performance measurement. This knowledge transfer scheme assists the convergence of the search process using previously constructed surrogates from multiple source problems, balancing exploration and exploitation.

Point cloud registration is a hot research topic in the field of computer vision. In recent years, the registration method based on evolutionary computation has attracted more and more attention because of its robustness to initial pose and flexibility of objective function design. However, most of the current evolutionary computation-based point cloud registration methods do not take into account the multi-view problem, that is, to capture the close relationship between point clouds from different perspectives. We fully realize that if these relations are used correctly, the registration performance can be improved. Therefore, this paper proposes an evolutionary multitasking multi-view point cloud registration method, which solves the problem of multi-view error accumulation. To ensure the unity of global and local, a two-level knowledge transfer strategy is proposed, which divides the multi-view cloud registration task into two levels. This strategy unifies the search space of two registration tasks, solves the negative transfer phenomenon, and avoids the problem of falling into the local optimum. Finally, the effectiveness of the method is verified by sufficient experiments. This method has strong robustness to noise and outliers, and can be effectively implemented in various registration scenarios.

Falls, a common type of accident, especially among the elderly and those with mobility impairments, potentially leading to serious physical injuries and health issues. Fall detection refers to the use of sensors, monitoring equipment, or other technological means to monitor and identify occurrences of falls. Currently, a lot of research on fall detection from various aspects such as vision, wearable devices, and multi-modal. However, current vision-based fall detection datasets are limited to single indoor scenarios and do not consider outdoor scenarios. Therefore, in this paper we propose a multi-perspective indoor and outdoor scenarios fall detection dataset based on campus surveillance, which include both indoor and outdoor campus scenarios. We employed YOLOv5 to carry out experiments on our proposed dataset as a benchmark. Moreover, we undertook comparative experiments against other datasets and assessed the richness of our dataset. The experiment results reveal that our dataset encompasses more diverse scenarios than other vision-based fall detection dataset.

The widespread dissemination of fake news across the internet has profound detrimental consequences for society, governments, and citizens. To address this pressing issue, numerous machine learning-based models have been developed for detecting fake news. However, the challenge of acquiring sufficient labeled news data in a new domain, coupled with the presence of inconsistent data distribution, necessitates the integration of unsupervised domain adaptation (DA) methods to enhance the reliability of cross-domain fake news detection. In this article, a rough-fuzzy graph learning DA for fake news detection is proposed. First, a rough-fuzzy graph learning method is proposed to effectively handle the representation of cross-domain sample uncertainty structural information, thereby learning a more discriminative subspace. Second, a rough-fuzzy region division strategy is designed to perform different analysis on target domain samples, thus achieving a more accurate description of the relationships between cross-domain samples. Furthermore, considering that domain private features may negatively affect the knowledge transfer process, a sparse structure preserving strategy is proposed to better capture shared general features across domains. Experimental evaluations conducted on three news datasets demonstrate the efficacy of the proposed method in cross-domain fake news detection.

Graph neural networks (GNNs) have shown the power in representation learning over graph-structured user-item interaction data for collaborative filtering (CF) task. However, with their inherently recursive message propagation among neighboring nodes, existing GNN-based CF models may generate indistinguishable and inaccurate user (item) representations due to the over-smoothing and noise effect with low-pass Laplacian smoothing operators. In addition, the recursive information propagation with the stacked aggregators in the entire graph structures may result in poor scalability in practical applications. Motivated by these limitations, we propose a simple and effective collaborative filtering model (SimRec) that marries the power of knowledge distillation and contrastive learning. In SimRec, adaptive transferring knowledge is enabled between the teacher GNN model and a lightweight student network, to not only preserve the global collaborative signals, but also address the over-smoothing issue with representation recalibration. Empirical results on public datasets show that SimRec archives better efficiency while maintaining superior recommendation performance compared with various strong baselines. Our implementations are publicly available at: https://github.com/HKUDS/SimRec.

Change detection based on heterogeneous images is challenging because of the distribution variance caused by imaging properties of different types of sensor. Most methods deal with this problem by transforming features into a common space. However, the lack of available labeled data limits the training of complex models and representation of heterogeneous distributions. In this paper, we propose to train a network via abundant unlabeled data by adopting cyclic adversarial pre-training in order to learn the relationship between heterogeneous distributions. After pre-training, for change detection, we introduce a constraint to maintain consistency of image content, to avoid the participation of changed pixels in training. Experiments on heterogeneous optical and SAR images prove the effectiveness of our proposed method.

In this paper, a method using fractional signatures for small target detection is proposed based on fusion of features extracted from both the time-frequency domain and fractional domain by using principal component analysis (PCA) to get the key characteristics for redundancy reduction. The process of reducing feature dimensions is visualized by the t-distributed stochastic neighbor embedding (t-SNE) network, also the simulation based on real dataset offers better performance in small target detection under sea clutter environment.

Change detection (CD) of remote sensing images is to detect the change region by analyzing the difference between two bitemporal images. It is extensively used in land resource planning, natural hazards monitoring and other fields. In our study, we propose a novel Siamese neural network for change detection task, namely Dual-UNet. In contrast to previous individually encoded the bitemporal images, we design an encoder differential-attention module to focus on the spatial difference relationships of pixels. In order to improve the generalization of networks, it computes the attention weights between any pixels between bitemporal images and uses them to engender more discriminating features. In order to improve the feature fusion and avoid gradient vanishing, multi-scale weighted variance map fusion strategy is proposed in the decoding stage. Experiments demonstrate that the proposed approach consistently outperforms the most advanced methods on popular seasonal change detection datasets.

Space-based radar has the characteristics of all-weather operation, and can accurately provide important data for understanding global environmental changes. On the other hand, with the rapid development of radio technology, space-based radar is facing more and more interference, such as terrestrial interference and inter-satellite interference, which greatly distort the measurements and degrade the image quality. In this paper, a novel interference mitigation method based on multi-temporal coupling analysis is proposed. The temporal-spatial coupling between time-series images could be modeled as low rank, while the interference follows the sparsity constraints due to the time-varying property. The interference extraction and mitigation on remote sensing images is realized by optimization by joint low-rank and sparsity regularization. The experimental results of Sentinel-1A data show that the method can achieve the separation of interference and restore clear remote sensing images with little distortion.

Deep learning based semantic segmentation is one of the popular methods in remote sensing image segmentation. In this paper, a network based on the widely used encoder-decoder architecture is proposed to accomplish the synthetic aperture radar (SAR) images segmentation. With the better representation capability of optical images, we propose to enrich SAR images with generated optical images via the generative adversative network (GAN) trained by numerous SAR and optical images. These optical images can be used as expansions of original SAR images, thus ensuring robust result of segmentation. Then the optical images generated by the GAN are stitched together with the corresponding real images. An attention module following the stitched data is used to strengthen the representation of the objects. Experiments indicate that our method is efficient compared to other commonly used methods.

Deep learning for change detection can provide effective guidance in many applications, such as agricultural development, urban planning, disaster avoidance, etc. In this study, a Siamese deep learning network based on High-Resolution Network (HRNet) is proposed to generate accurate results. HRNet can integrate multi-dimensional features and output high-resolution results which have attracted attention due to its reliable feature extraction ability. In this paper, we extract the feature pairs of several different dimensions, including the two features behind the down-sampling in the stem stage which is an important part of HRNet. Moreover, feature ex-traction and intensive up-sampling tasks are completed by using a variety of feature fusion sub-networks, which are used to enhance the learning ability. Experiments show the superiority of the proposed Siamese HRNet on a widely used change detection dataset.

Synthetic Aperture Radar is superior to optical sensors in that it can identify ships at all hours and on all days. Deep learning-based object detection relies on huge amounts of data, yet SAR ship images are challenging to obtain and label. A few-shot cross-domain transfer learning approach for SAR image ship detection is used in this paper. It is divided into two stages: the first uses a large volume of optical remote sensing ship images as the source domain training detection framework, and the second employs SAR ship images and optical remote sensing ship images to create a few-shot balanced subset fine-tuning detection framework. Use a metric learning-based prediction box classifier instead of a fully connected prediction box classifier. When fine-tuning the whole detection frame using the metric learning-based pre-diction frame classifier, the experiments show that an AP50 of 55.99% can be reached with only 10 SAR ship images.

Deep learning methods have been widely used in the task of hyperspectral pansharpening. However, most of these methods regard the Panchromatic (PAN) image as a kind of auxiliary information, which is mainly used as spatial details to add on the hyperspectral image (HSI) after processing. Obviously, this kind of methods utilize the PAN image insufficiently, resulting in the imbalance of spatial preservation and spatial preservation. In this paper, a spectral constrained residual attention network (SCRAN) is proposed by using the PAN image as the foundation of the pansharpening task and concerning on the spectral and spatial learning. The proposed SCRAN method consists of three parts: a spectral feature extraction net, an attention spatial residual net and a spectral reconstruction net. A spectral constrained loss function is designed to enhance the spectral learning ability of SCRAN. Additionally, in SCRAN, a deep back-projection network (DBPN) is operated to upsample the HSI, and the histogram matching is applied to the PAN image to make it closer to the HSI in terms of spectral bands.

In this paper, we propose a new change detection method of multi-temporal synthetic aperture radar (SAR) images. Due to the ability of extracting key feature of images and robustness to noise, stacked denoising auto encoder (SDAE) has been widely used in remote sensing. However, the single SDAE stills has some limitations to handle with the speckle noise of SAR images. Therefore, we propose a new structure Bi-hierarchical SDAE for feature extraction. The first level of SDAE denoises the original image and reconstructs the difference map, and the second level extracts the superpixel-based difference features for classification. Besides, Bayesian optimization effectively improves the classification performance of feature classifier. The experimental results of the datasets in this paper show that the Bi-hierarchical SDAE and Bayesian optimization framework has high accuracy and proves its effectiveness.

In recent years, convolutional neural networks (CNNs) have shown excellent effectiveness on hyperspectral image classification (HSI) tasks. However, it is a challenge to design a suitable CNN architecture to obtain great performance according to different tasks. Different from the traditional manual design, in this paper, an evolutionary multitasking CNN architecture search framework for HSI classification is proposed to search the optimal architectures and accomplish classification of different tasks simultaneously. Through encoding the CNN architectures, the proposed algorithm is able to achieve global search in the same search space and select well-adapted individuals for evolution. In the evolutionary multitasking environment, information can be transferred between and within tasks, which can accelerate the convergence and explore good architectures through beneficial transfer. In the experiments, the effectiveness of the proposed method is demonstrated by the comparison with different methods on two common data sets.

With abundant temporal, spectral, and spatial information, multispectral images are proficient for acquiring a superior comprehension of the Earth’s condition and its changes, which enables the achievement of multiple change detection (CD) tasks. However, high temporal, spatial, and spectral information of data brings obstacles to perform multiple change analysis due to the lack of effective feature extraction operation. In addition, the traditional multiple CD methods rely too much on manual participation. Here, a generative representation learning network (GRN) and a cyclic clustering technique are combined into a unified model, which is driven to learn spatial–temporal–spectral features for unsupervised multiple CD. GRN aims to efficiently extract and merge robust difference information with a recurrent learning mechanism for self-adaptive classification refinement, in which different types of changes can be identified and highlighted. Furthermore, a cyclic training strategy is designed to refine the clustering-friendly features, in which similar change types are gradually merged into the same classes. Meanwhile, the number of change types will be optimized through a self-adaptive way and eventually converge to its stable state, which is close to the real distribution. Experimental results on real multispectral datasets demonstrate the effectiveness and superiority of the proposed model on multiple CD.

Traditional band selection methods only analyze one dataset at a time and start searching band subsets from the zero ground state of knowledge, which cannot effectively mine spectral information to guide band selection. However, for hyperspectral images (HSIs) obtained by the same sensor, the spectral information has a similar physical meaning (radiance or reflectivity). Collaborative analysis technology can analyze multiple hyperspectral datasets to explore the inherent spectral features shared among them. In this letter, a multiple datasets collaborative analysis framework for hyperspectral band selection is proposed to realize spectral information communication, thereby guiding and promoting the band selection of each dataset. Different band selection tasks are established pertinently, and then, the evolutionary multitasking band selection method is designed to facilitate the knowledge sharing of different band selection tasks. More importantly, the interaction mechanism among different datasets is adjusted dynamically, thereby improving the cooperation ability of the collaborative analysis framework. Besides, a predominant gene reservation crossover and a deduplication mutation are designed for retaining the promising bands and avoiding the selection of repeat bands. Experiments indicate that the proposed collaborative analysis method works more efficiently than the comparison methods and successfully enhances accuracy and convergence compared to single dataset analysis.

Despite the great advantages in deep feature representation when dealing with change detection (CD) problem, the designs of neural networks were time-consuming processes of trial and error. In addition, the traditional CD methods based on deep neural networks (DNN) only deal with one dataset at a time, which has limited learning knowledge and undoubtedly fails to take advantage of the common characteristics among similar datasets. For hyperspectral images (HSIs) obtained by the same sensor, the spectral information has a similar physical meaning (radiance or reflectivity). To utilize the inherent similarity within hyperspectra for learning a robust difference signature, a collaborative analysis framework with self-perception network architecture (SPNA-CA) is proposed to efficiently learn from multiple datasets and leverage their synergy. Different network architecture searching tasks are established for each dataset pertinently, in which the evolutionary multitasking self-perception network architecture (SPNA) method is designed for exploring effective and reasonable network architectures. Besides, a cross-task knowledge transfer mechanism (CKTM) is proposed to transfer excellent network architecture information, which improves the efficiency of the collaborative analysis framework. Experimental results confirm the effectiveness of collaborative analysis for solving HSI-CD problems among multiple datasets.

Nowadays, due to the difficult acquisition of true labels, a semisupervised neural network has shown great potential for change detection (CD) in remote sensing images. However, most of the traditional semisupervised neural network detection frameworks are complex to train and require additional structural analysis, along with a fixed structure, lacking universality. In this article, a semisupervised adaptive ladder network (SSALN) for remote sensing image CD is proposed, which enables dual-input label-incremental architecture searching with a concise and variable structure. First, SSALN is suitable for CD from two remote sensing images of any type with the characteristic of minimal label dependency and automatic network structure adjustment. The network can generate more reliable pseudolabels through continuous iterations to help limited real labels exploit implicit information, identify the most effective network, and form the ascending network structure optimization. Second, the acquisition of pseudolabels is the fusion of semisupervised and unsupervised CD approaches, which ensures the multiperspective information supplement. Multiple CD maps are fused to generate labels for the next iteration, making the predicting more reliable. Finally, both homogenous images and heterogenous images are tested with experiments. Even if the detection object is switched, it can be well adaptive and compatible without manual modification of the network. Experimental results demonstrate that the proposed method can promote the flow of label information through structure searching and self-circulation in the ascending network optimization; thus, it has outstanding performance on tasks of remote sensing image CD.

Unsupervised domain adaptation aims to learn an accurate classifier for a target domain by leveraging knowledge learned from a related (source) domain. Existing approaches focus on deriving new domain-invariant feature representations to align two domains and an extra classifier is required. In this paper, we propose a novel unsupervised domain adaptation method to train a classifier directly for the target domain without learning the domain-invariant feature representation. For our method, the pseudo labels are assigned to target samples. An effective method is proposed to measure the relationship among cross-domain samples more accurately, so that we can construct a$p$-nearest neighbor graph. Then label propagation is employed to update the target sample labels. The graph model and labels of target samples are expected to be updated alternately within an iterative framework. To further improve the classifier, a fuzzy classification and pseudo-label selection mechanism are utilized. Extensive experiments validate that our proposed method is superior or comparable to the state-of-the-art unsupervised domain adaptation methods.

In the last few years, neural networks were introduced to change detection for a better understanding of remote sensing images. However, the designs of these neural networks were time-consuming processes of trial and error, which failed to account for their validity. Thus, a simple and efficient change detection method based on network architecture search in terms of the evolutionary algorithm is proposed to deal with SAR images change detection problems. In the proposed method, an efficient gene encoding is applied to represent the unpredictable optimal depth and the number of neurons in each hidden layer. Besides, a combinatorial evaluation strategy and a self-adaptive network solution selection are designed for effective and reasonable network architectures. What is more, a hidden layer random alignment crossover operator and a drawing lots mutation operator are designed for the enhancement of diversity of network architectures. Experimental results on a few SAR image data sets demonstrate that the proposed method can generate appropriate networks to solving SAR images change detection.

Domain adaptation aims at learning from the la-beled source domain to build an accurate classifier for a related but different target domain. Existing methods attempt to reduce domain discrepancy explicitly by means of statistical properties yet ignore the inherent differences among samples. In this paper, we present a novel solution for domain adaptation based on collaborative representation, named Discrepancy-Aware Collaborative Representation (DACR). Inspired by the success of nearest regularization, DACR develops a novel indicator to measure the discrepancy among every source sample and target domain. Then the indicator is employed in sparse regularization thus ensure that samples with small discrepancy have larger weights in the learned representation. Extensive experiments verify that DACR is able to achieve comparable performance with existing methods while significantly reducing computing complexity.

There is a ruling maxim in deep learning land, bigger is better. However, bigger neural network provides higher performance but also expensive computation, memory and energy. The simplified model which preserves the accuracy of original network arouses a growing interest. A simple yet efficient method is pruning, which cuts off unimportant synapses and neurons. Therefore, it is crucial to identify important parts from the given numerous connections. In this paper, we use the evolutionary pruning method to simplify the structure of deep neural networks. A multi-objective neural networks pruning model which balances the accuracy and the sparse ratio of networks is proposed and we solve this model with particle swarm optimization (PSO) method. Furthermore, we fine-tune the network which is obtained by pruning to obtain better pruning result. The framework of alternate pruning and fine-tuning operations is used to achieve more prominent pruning effect. In experimental studies, we prune LeNet on MNIST and shallow VGGNet on CIFAR-10. Experimental results demonstrate that our method could prune over 80% weights in general with no loss of accuracy.

Evolutionary multi-task optimization (EMTO) studies on how to simultaneously solve multiple optimization problems, so-called component problems, via evolutionary algorithms, which has drawn much attention in the field of evolutionary computation. Knowledge transfer across multiple optimization problems (being solved) is the key to make EMTO to outperform traditional optimization paradigms. In this work, we propose a simple and effective knowledge transfer strategy which utilizes the best solution found so far for one problem to assist in solving the other problems during the optimization process. This strategy is based on random replacement. It does not introduce extra computational cost in terms of objective function evaluations for solving each component problem. However, it helps to improve optimization effectiveness and efficiency, compared to solving each component problem in a standalone way. This light-weight knowledge transfer strategy is implemented via differential evolution within a multi-population based EMTO paradigm, leading to a differential evolutionary multi-task optimization (DEMTO) algorithm. Experiments are conducted on the CEC'2017 competition test bed to compare the proposed DEMTO algorithm with five state-of-the-art EMTO algorithms, which demonstrate the superiority of DEMTO.

Based on the fuzzy suppressed c-means clustering algorithm, a new method is developed for extracting auroral oval regions from images acquired by the Ultraviolet Imager aboard the POLAR satellite. Compared with different variations of fuzzy c-means clustering methods, suppressed fuzzy c-means clustering was proposed with the intention of improving convergence rate by modifying membership values, which is more suitable for studying auroral behavior over time with considering a series of images. However, traditional suppressed c-means clustering algorithms employ the same suppressed parameter for modifying fuzzy membership degrees of all pixels, ignoring the fact that image characteristics varies from one auroral oval images to another. In this paper, the technique parameters which is set beforehand will be automatically selected according to the intrinsic characteristic of each auroral oval image. Moreover, corresponding operations are devised for modifying membership values of different pixels according to their real needs, which makes it clear to decide whether to proceed with further determination or just make decision on the basis of already obtained analysis results. Experimental results on auroral oval images acquired from an online database collected by NASA Polar satellite's Ultraviolet Imager indicate that the proposed method extracts more accurate auroral oval regions than traditional suppressed c-means clustering method in most cases.

Image change detection is to recognize the changes between two images that are taken over the same scene but at different times, which has been applied broadly in many fields. Fuzzy clustering is a frequently-used technique for unsupervised change detection. However, traditional fuzzy clustering algorithms are easy to be trapped into a local optimum due to the limits of their optimization processes. To tackle the problem, a novel differential evolution algorithm with an automatically learning selection strategy is proposed in this paper. Different from the selection rules of classical differential evolution algorithm, this method firstly pre-classifies all original individuals and trial individuals according to the scope of the individual fitness at each generation, which will preliminarily determine whether they are selected for the next generation. Secondly, in order to increase the diversity of the population, we choose a few individuals from the non-selected population with a low probability into selected ones. Finally, the samples including partial individuals from the selected and non-selected lists are used to train the neural networks that will learn the selection strategy. This method will learn different selection strategies in every generation, which will significantly accelerate the convergence speed. The proposed change detection method, combining fuzzy clustering with newly designed differential evolution algorithm, show excellent performance. Experiments conducted on Synthetic Aperture Radar images have demonstrated the superiority of the proposed method.

Neural networks are currently popular learning models to represent and analyze data. We address two issues about that in this paper. On the one hand, the parameters between neurons are often restricted to be constants, which greatly limits the learning ability and reduces the robustness of the neural network. For that, it is necessary to make the parameters fuzzy. In this paper, we introduce the fuzzy set theory to neural networks where the parameters are expressed by fuzzy numbers. Meanwhile, the loss term and sparsity of the network become fuzzy. On the other hand, a user-defined weighting parameter need to be determined to keep the trade-off between the fuzzy loss term and fuzzy sparsity. In order to solve the two issues simultaneously, the main contribution of this paper is to combine fuzzy set theory with multi-objective sparsity to apply to neural networks, for the first time, and propose a fuzzy multi-objective sparse feature learning (FMSFL) model, where a multi-objective optimization model is established, and reconstruction error and sparsity of fuzzy model are considered as two objectives. In the experiments, we demonstrate the effectiveness of our model, and both learning capability and robustness of the neural networks based on the proposed model are improved.

The proportion of an aurora region in a field of view is an important index to measure the magnetic stress stored in the magnetosphere. Detecting the aurora region is a necessary step to obtain the index. Intensity inhomogeneity, a characteristic of overlaps between the ranges of intensities in segmented regions, has become a challenging issue in the field of auroral oval segmentation. Classical auroral segmentation methods can reasonably detect auroral ovals in clean images. The segmentation quality of these methods deteriorates when auroral oval pixel intensities are not distinct from the background. To reduce the negative influence of intensity inhomogeneity in auroral oval segmentation, a gradually evolved active contour model employing the narrow-band technique instead of using a full region computation is designed. In such case, only the region near auroral oval boundaries has to evolve in each iteration, thus enabling the contour to evolve gradually and saving computational resources. Experimental results demonstrate that the proposed method detects more accurate auroral oval regions than traditional methods in terms of human visual perception and segmentation accuracy.

Social networks are effective tools for analyzing many social topics in sociology. In the past few decades, a great deal of efforts have been made to study the balance property of social networks. This paper presents a novel bi-objective model for social network structural balance, and a multiobjective discrete particle swarm optimizer is used to optimize the bi-objective model. Each single run of the algorithm can yield a set of Pareto solutions, each of which represents a certain network partition that divides a signed network into many clusters. Consequently, by simultaneously optimizing the objectives in the proposed model, one may have many choices to analyze the balance problem. Extensive experiments compared against several other models and algorithms have been done. All the experiments indicate that the proposed model is helpful for social network structural balance analytics, and that the algorithm is effective.

In this paper, we propose a novel approach for detecting multiple changes from two multi-temporal images. Despite the development of the change vector analysis (CVA) framework and its improved version the compressed CVA (C2VA) framework, it is found that they are limited when tackling the multi-change detection task for the images with one channel. Also, the intensity itself is fragile due to the existing noise, which especially influences the detection of subtle changes. Therefore, the stacked denosing autoencoder (SDAE) which serves as a fine tool for feature extraction is employed to generate a multi-dimensional feature representations. In this way, the C2VA framework can be applied to the inner robust features so that a satisfactory performance can be guaranteed. Experimental results from two datasets show its high accuracy and moderate time complexity, which demonstrates the effectiveness of the proposed SDAE-C2VA approach.

Due to the noise interference and redundancy in multispectral images, it is promising to transform the available spectral channels into a suitable feature space for relieving noise and reducing the redundancy. The booming of deep learning provides a flexible tool to learn abstract and invariant features directly from the data in their raw forms. In this letter, we propose an unsupervised change detection technique for multispectral images, in which we combine deep belief networks (DBNs) and feature change analysis to highlight changes. First, a DBN is established to capture the key information for discrimination and suppress the irrelevant variations. Second, we map bitemporal change feature into a 2-D polar domain to characterize the change information. Finally, an unsupervised clustering algorithm is adopted to distinguish the changed and unchanged pixels, and then, the changed types can be identified by classifying the changed pixels into several classes according to the directions of feature changes. The experimental results demonstrate the effectiveness and robustness of the proposed method.

Edge-directed interpolation is widely used to enhance visual performance of remote sensing image. Compared with traditional bi-cubic interpolation and bilinear interpolation, a great number of matrix operations will appear as it is getting better visual performance. CUDA (Compute Unified Device Architecture) offers tremendous performance in many high-performance computing areas. Edge-directed interpolation can be mapped to this architecture (CUDA) readily. However, parallel schemes based on CUDA are generally decomposed into coarse-grained tasks, which is suitable for thread blocks. In this paper, a parallel approach of fine-grained edge-directed interpolation is proposed. Based on CUDA, the process of parallel interpolation for one missing pixel is assigned to 4*4 threads for the reason that majority of matrix operations are related to 4*4 matrix. This task division strategy minimizes resource pressure of thread-blocks. Our calculating scheme is expressed in terms of increasing parallelism that is efficiently implemented on the GPU. By employing one NVIDIA GTX480 GPU and one NVIDIA GTX590 GPU in the case with asynchronous I/O transfer, our GPU optimization efforts on fine-grained edge-directed interpolation scheme finally achieve a speedup of 69.8x with respect to its CPU counterpart C code running on one CPU core of Intel core(TM) i7-920.

In this paper, we present an improved fuzzy C-means (FCM) algorithm for image segmentation by introducing a tradeoff weighted fuzzy factor and a kernel metric. The tradeoff weighted fuzzy factor depends on the space distance of all neighboring pixels and their gray-level difference simultaneously. By using this factor, the new algorithm can accurately estimate the damping extent of neighboring pixels. In order to further enhance its robustness to noise and outliers, we introduce a kernel distance measure to its objective function. The new algorithm adaptively determines the kernel parameter by using a fast bandwidth selection rule based on the distance variance of all data points in the collection. Furthermore, the tradeoff weighted fuzzy factor and the kernel distance measure are both parameter free. Experimental results on synthetic and real images show that the new algorithm is effective and efficient, and is relatively independent of this type of noise.