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Video Summarization (VS) has become one of the most effective solutions for quickly understanding a large volume of video data. Dictionary selection with self representation and sparse regularization has demonstrated its promise for VS by formulating the VS problem as a sparse selection task on video frames. However, existing dictionary selection models are generally designed only for data reconstruction, which results in the neglect of the inherent structured information among video frames. In addition, the sparsity commonly constrained by $L_{2,1}$ norm is not strong enough, which causes the redundancy of keyframes, i.e., similar keyframes are selected. Therefore, to address these two issues, in this paper we propose a general framework called graph convolutional dictionary selection with $L_{2,p}$ ($0< p\leq 1$) norm (GCDS $_{2,p}$) for both keyframe selection and skimming based summarization. Firstly, we incorporate graph embedding into dictionary selection to generate the graph embedding dictionary, which can take the structured information depicted in videos into account. Secondly, we propose to use $L_{2,p}$ ($0< p\leq 1$) norm constrained row sparsity, in which $p$ can be flexibly set for two forms of video summarization. For keyframe selection, $0< p< 1$ can be utilized to select diverse and representative keyframes; and for skimming, $p=1$ can be utilized to select key shots. In addition, an efficient iterative algorithm is devised to optimize the proposed model, and the convergence is theoretically proved. Experimental results including both keyframe selection and skimming based summarization on four benchmark datasets demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Spectral unmixing expresses the mixed pixels existing in hyperspectral images as the product of endmembers and their corresponding fractional abundances, which has been widely used in hyperspectral imagery analysis. However, the endmember spectra even for pixels from the same material of an image may include variability due to the influence of lighting conditions and inherent properties of materials within different pixels. Though the in situ spectral library has been used to accommodate such variability by using multiple in situ spectra to represent each kind of material, the performance improvement may be restricted due to the limited number of endmembers for each material. Therefore, in this article, spectral variability is directly extracted from an in situ endmember library and considered to be transferable among different endmembers for the first time. Furthermore, such a spectral variability is further used to augment sparse unmixing by synchronously performing endmember-based reconstruction and spectral variability-augmented reconstruction in the sparse unmixing model. By, respectively, imposing sparse and smoothness regularization over abundances and variability coefficients, a convex optimization-based spectral variability augmented sparse unmixing (SVASU) is finally proposed, and its convergence performance is also analyzed. Experiments conducted over synthetic and real-world datasets demonstrate that the proposed SVASU method not only significantly improves the unmixing performance of conventional spectral library-based unmixing but also outperforms several state-of-the-art sparse unmixing algorithms. [ABSTRACT FROM AUTHOR]

Video summarization (VS) is generally formulated as a subset selection problem where a set of representative keyframes or key segments is selected from an entire video frame set. Though many sparse subset selection based VS algorithms have been proposed in the past decade, most of them adopt linear sparse formulation in the explicit feature vector space of video frames, and don’t consider the local or global relationships among frames. In this paper, we first extend the conventional sparse subset selection for VS into kernel block sparse subset selection (KBS3) to utilize the advantage of kernel sparse coding and introduce a local inter-frame relationship through packing of frame blocks. Going a step further, we propose a similarity based block sparse subset selection (SB2S3) model by applying a specially designed transformation matrix on the KBS3 model in order to introduce a kind of global inter-frame relationship through the similarity. Finally, a greedy pursuit based algorithm is devised for the proposed NP-hard model optimization. The proposed SB2S3 has the following advantages: 1) through the similarity between each frame and any other frame, the global relationship among all frames can be considered; 2) through block sparse coding, the local relationship of adjacent frames is further considered; and 3) it has a wider application, since features can derive similarity, but not vice versa. It is believed that the effect of modeling such global and local relationships among frames in this paper, is similar to that of modeling the long-range and short-range dependencies among frames in deep learning based methods. Experimental results on three benchmark datasets have demonstrated that the proposed approach is superior to not only other sparse subset selection based VS methods but also most unsupervised deep-learning based VS methods. [ABSTRACT FROM AUTHOR]

Rotation-invariant features are of great importance for object detection and image classification in very-high-resolution (VHR) optical remote sensing images. Though multibranch convolutional neural network (mCNN) has been demonstrated to be very effective for rotation-invariant feature learning, how to effectively train such a network is still an open problem. In this article, a nested Siamese structure (NSS) is proposed for training the mCNN to learn effective rotation-invariant features, which consists of an inner Siamese structure to enhance intraclass cohesion and an outer Siamese structure to enlarge interclass margin. Moreover, a double center loss (DCL) function, in which training samples from the same class are mapped closer to each other while those from different classes are mapped far away to each other, is proposed to train the proposed NSS even with a small amount of training samples. Experimental results over three benchmark data sets demonstrate that the proposed NSS trained by DCL is very effective to encounter rotation varieties when learning features for image classification and outperforms several state-of-the-art rotation-invariant feature learning algorithms even when a small amount of training samples are available. [ABSTRACT FROM AUTHOR]

Many applications have benefited from the images with both high spatial and spectral resolution, such as mineralogy and surveillance. However, it is difficult to acquire such images due to the limitation of sensor technologies. Recently, super-resolution (SR) techniques have been proposed to improve the spatial or spectral resolution of images, e.g., improving the spatial resolution of hyperspectral images (HSIs) or improving spectral resolution of color images (reconstructing HSIs from RGB inputs). However, none of the researches attempted to improve both spatial and spectral resolution together. In this article, these two types of resolution are jointly improved using convolutional neural network (CNN). Specifically, two kinds of CNN-based SR are conducted, including a simultaneous spatial–spectral joint SR (SimSSJSR) that conducts SR in spectral and spatial domain simultaneously and a separated spatial–spectral joint SR (SepSSJSR) that considers spectral and spatial SR sequentially. In the proposed SimSSJSR, a full 3-D CNN is constructed to learn an end-to-end mapping between a low spatial-resolution mulitspectral image (LR-MSI) and the corresponding high spatial-resolution HSI (HR-HSI). In the proposed SepSSJSR, a spatial SR network and a spectral SR network are designed separately, and thus two different frameworks are proposed for SepSSJSR, namely SepSSJSR1 and SepSSJSR2, according to the order that spatial SR and spectral SR are applied. Furthermore, the least absolute deviation, instead of mean square error (MSE) in traditional SR networks, is chosen as the loss function for the proposed networks. Experimental results over simulated images from different sensors demonstrated that the proposed SepSSJSR1 is most effective to improve spatial and spectral resolution of MSIs sequentially by conducting spatial SR prior to spectral SR. In addition, validation on real Landsat images also indicates that the proposed SSJSR techniques can make full use of available MSIs for high-resolution-based analysis or applications. [ABSTRACT FROM AUTHOR]

The explosive growth of video data has raised new challenges for many video processing tasks such as video browsing and retrieval, hence, effective and efficient video summarization (VS) is urgently demanded to automatically summarize a video into a succinct version. Recent years have witnessed the advancements of sparse representation based approaches for VS. However, video frames are analyzed individually for keyframe selection in existing methods, which could lead to redundancy among selected keyframes and poor robustness to outlier frames. Due to that adjacent frames are visually similar, candidate keyframes often occur in temporal blocks, in addition to sparse presence. Therefore, in this paper, the block-sparsity of candidate keyframes is taken into consideration, by which the VS problem is formulated as a block sparse dictionary selection model. Moreover, a simultaneous block version of Orthogonal Matching Pursuit (SBOMP) algorithm is designed for model optimization. Two keyframe selection strategies are also explored for each block. Experimental results on two benchmark datasets, namely VSumm and TVSum datasets, demonstrate that the proposed SBOMP based VS method clearly outperforms several state-of-the-art sparse representation based methods in terms of F-score, redundancy among keyframes and robustness to outlier frames. [ABSTRACT FROM AUTHOR]

Feature learning technologies using convolutional neural networks (CNNs) have shown superior performance over traditional hand-crafted feature extraction algorithms. However, a large number of labeled samples are generally required for CNN to learn effective features under classification task, which are hard to be obtained for hyperspectral remote sensing images. Therefore, in this paper, an unsupervised spatial–spectral feature learning strategy is proposed for hyperspectral images using 3-Dimensional (3D) convolutional autoencoder (3D-CAE). The proposed 3D-CAE consists of 3D or elementwise operations only, such as 3D convolution, 3D pooling, and 3D batch normalization, to maximally explore spatial–spectral structure information for feature extraction. A companion 3D convolutional decoder network is also designed to reconstruct the input patterns to the proposed 3D-CAE, by which all the parameters involved in the network can be trained without labeled training samples. As a result, effective features are learned in an unsupervised mode that label information of pixels is not required. Experimental results on several benchmark hyperspectral data sets have demonstrated that our proposed 3D-CAE is very effective in extracting spatial–spectral features and outperforms not only traditional unsupervised feature extraction algorithms but also many supervised feature extraction algorithms in classification application. [ABSTRACT FROM AUTHOR]

Arising from various environmental and atmos- pheric conditions and sensor interference, spectral variations are inevitable during hyperspectral remote sensing, which degrade the subsequent hyperspectral image analysis significantly. In this paper, we propose simultaneous spatial and spectral low-rank representation (S3LRR) that can effectively suppress the within-class spectral variations for classification purposes. The S3LRR recovers an intrinsic component with the same dimension as the original image, in which both spatial and spectral low-rank priors are adopted to regularize the intrinsic component simultaneously and compensate to each other, together with robust modeling of spectral variations. Compared with existing methods that explore only the spectral low-rank prior, the novel spatial low-rank prior (i.e., low-rank prior in band-wise) can take the spatial structure information of hyperspectral images into account, which has demonstrated to be very useful. Technically, we formulate S3LRR as a constrained convex optimization problem, and solve it using the efficient inexact augmented Lagrangian multiplier method. The resulting intrinsic component is less interfered by within-class spectral variations, and more discriminatory to offer higher classification accuracy. Comprehensive experiments on benchmark data sets demonstrate that the proposed S3LRR improves classification accuracy significantly, which outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN) is well known for its capability of feature learning and has made revolutionary achievements in many applications, such as scene recognition and target detection. In this paper, its capability of feature learning in hyperspectral images is explored by constructing a five-layer CNN for classification (C-CNN). The proposed C-CNN is constructed by including recent advances in deep learning area, such as batch normalization, dropout, and parametric rectified linear unit (PReLU) activation function. In addition, both spatial context and spectral information are elegantly integrated into the C-CNN such that spatial-spectral features are learned for hyperspectral images. A companion feature-learning CNN (FL-CNN) is constructed by extracting fully connected feature layers in this C-CNN. Both supervised and unsupervised modes are designed for the proposed FL-CNN to learn sensor-specific spatial-spectral features. Extensive experimental results on four benchmark data sets from two well-known hyperspectral sensors, namely airborne visible/infrared imaging spectrometer (AVIRIS) and reflective optics system imaging spectrometer (ROSIS) sensors, demonstrate that our proposed C-CNN outperforms the state-of-the-art CNN-based classification methods, and its corresponding FL-CNN is very effective to extract sensor-specific spatial-spectral features for hyperspectral applications under both supervised and unsupervised modes. [ABSTRACT FROM PUBLISHER]

Over the past decade, deep learning techniques have significantly advanced the field of building change detection in remote sensing imagery. However, existing deep learning-based approaches often encounter limitations in complex remote sensing scenarios, resulting in false detections and detail loss. This paper introduces EFP-Net, a novel building change detection approach that resolves the mentioned issues by utilizing effective feature fusion and foreground perception. EFP-Net comprises three main modules, the feature extraction module (FEM), the spatial–temporal correlation module (STCM), and the residual guidance module (RGM), which jointly enhance the fusion of bi-temporal features and hierarchical features. Specifically, the STCM utilizes the temporal change duality prior and multi-scale perception to augment the 3D convolution modeling capability for bi-temporal feature variations. Additionally, the RGM employs the higher-layer prediction map to guide shallow layer features, reducing the introduction of noise during the hierarchical feature fusion process. Furthermore, a dynamic Focal loss with foreground awareness is developed to mitigate the class imbalance problem. Extensive experiments on the widely adopted WHU-BCD, LEVIR-CD, and CDD datasets demonstrate that the proposed EFP-Net is capable of significantly improving accuracy in building change detection. [ABSTRACT FROM AUTHOR]

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. In this paper, we formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem. That is, the original video sequence can be best reconstructed with as few selected keyframes as possible. Different from the recently proposed convex relaxation based sparse dictionary selection method, our proposed method utilizes the true sparse constraint L 0 norm, instead of the relaxed constraint L 2 , 1 norm, such that keyframes are directly selected as a sparse dictionary that can well reconstruct all the video frames. An on-line version is further developed owing to the real-time efficiency of the proposed MSR principle. In addition, a percentage of reconstruction (POR) criterion is proposed to intuitively guide users in obtaining a summary with an appropriate length. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed methods outperform the state of the art. [ABSTRACT FROM AUTHOR]

Endmember extraction (EE) has been widely utilized to extract spectrally unique and singular spectral signatures for spectral mixture analysis of hyperspectral images. Recently, spatial–spectral EE (SSEE) algorithms have been proposed to achieve superior performance over spectral EE (SEE) algorithms by taking both spectral similarity and spatial context into account. However, these algorithms tend to neglect anomalous endmembers that are also of interest. Therefore, in this paper, an improved SSEE (iSSEE) algorithm is proposed to address such limitation of conventional SSEE algorithms by accounting for both anomalous and normal endmembers. By developing simplex projection and simplex complementary projection, all the hyperspectral pixels are projected into a simplex determined by the normal endmembers extracted in conventional SSEE algorithms. As a result, anomalous endmembers are identified iteratively by utilizing the l2^{\infty} norm to find the maximum simplex complementary projection. In order to determine how many anomalous endmembers are to be extracted, a novel Residual-be-Noise Probability-based algorithm is also proposed by elegantly utilizing the spatial-purity map generated in the previous SSEE step. Experimental results on both synthetic and real datasets demonstrate that simplex projection errors can be significantly reduced by identifying both anomalous and normal endmembers in the proposed iSSEE algorithm. It is also confirmed that the performance of the proposed iSSEE algorithm clearly outperforms that of SEE algorithms since both spatial context and spectral similarity are utilized. [ABSTRACT FROM AUTHOR]

Spectral mixture analysis (SMA) has been widely utilized to address the mixed-pixel problem in the quantitative analysis of hyperspectral remote sensing images, in which endmember extraction (EE) plays an extremely important role. In this paper, a novel algorithm is proposed to integrate both spectral similarity and spatial context for EE. The spatial context is exploited from two aspects. At first, initial endmember candidates are identified by determining the spatial purity (SP) of pixels in their spatial neighborhoods (SNs). Several SP measurements are investigated at both intensity level and feature level. In order to alleviate local spectra variability, the average of the pixels in pure SNs are voted as endmember candidates. Then, the spatial connectivity is utilized to merge spatially related endmember candidates by finding connection paths in a graph so that the number of endmember candidates is further reduced, which results in computational efficiency and better performance in SMA by alleviating global spectral variability. Experimental results on both synthetic and real hyperspectral images demonstrate that the proposed SP based EE (SPEE) algorithm outperforms the other popular EE algorithms. It is also observed that feature-level SP measurements are more distinguishable than intensity-level SP measurements to discriminate pure SNs from mixed SNs. [ABSTRACT FROM AUTHOR]

Hyperspectral imagery (HSI) with high spectral resolution contributes to better material discrimination, while the spatial resolution limited by the sensor technique prevents it from accurately distinguishing and analyzing targets. Though generative adversarial network-based HSI super-resolution methods have achieved remarkable progress, the problems of treating vital and unessential features equally in feature expression and training instability still exist. To address these issues, an attention-enhanced generative adversarial network (AEGAN) for HSI spatial super-resolution is proposed, which elaborately designs the enhanced spatial attention module (ESAM) and refined spectral attention module (RSAM) in the attention-enhanced generator. Specifically, the devised ESAM equipped with residual spatial attention blocks (RSABs) facilitates the generator that is more focused on the spatial parts of HSI that are difficult to produce and recover, and RSAM with spectral attention refines spectral interdependencies and guarantees the spectral consistency at the respective pixel positions. Additionally, an especial U-Net discriminator with spectral normalization is enclosed to pay more attention to the detailed informations of HSI and yield to stabilize the training. For producing more realistic and detailed super-resolved HSIs, an attention-enhanced generative loss is constructed to train and constrain the AEGAN model and investigate the high correlation of spatial context and spectral information in HSI. Moreover, to better simulate the complicated and authentic degradation, pseudo-real data are also generated with a high-order degradation model to train the overall network. Experiments on three benchmark HSI datasets illustrate the superior performance of the proposed AEGAN method in HSI spatial super-resolution over the compared methods. [ABSTRACT FROM AUTHOR]

Detecting sparse, small, lost persons with only a few pixels in high-resolution aerial images was, is, and remains an important and difficult mission, in which a vital role is played by accurate monitoring and intelligent co-rescuing for the search and rescue (SaR) system. However, many problems have not been effectively solved in existing remote-vision-based SaR systems, such as the shortage of person samples in SaR scenarios and the low tolerance of small objects for bounding boxes. To address these issues, a copy-paste mechanism (ISCP) with semi-supervised object detection (SSOD) via instance segmentation and maximum mean discrepancy distance is proposed (MMD), which can provide highly robust, multi-task, and efficient aerial-based person detection for the prototype SaR system. Specifically, numerous pseudo-labels are obtained by accurately segmenting the instances of synthetic ISCP samples to obtain their boundaries. The SSOD trainer then uses soft weights to balance the prediction entropy of the loss function between the ground truth and unreliable labels. Moreover, a novel evaluation metric MMD for anchor-based detectors is proposed to elegantly compute the IoU of the bounding boxes. Extensive experiments and ablation studies on Heridal and optimized public datasets demonstrate that our approach is effective and achieves state-of-the-art person detection performance in aerial images. [ABSTRACT FROM AUTHOR]

Hyperspectral videos (HSVs) can record more adequate detail clues than other videos, which is especially beneficial in cases of abundant spectral information. Although traditional methods based on correlation filters (CFs) employed to explore spectral information locally achieve promising results, their performances are limited by ignoring global information. In this paper, a joint spectral–spatial information method, named spectral–spatial transformer-based feature fusion tracker (SSTFT), is proposed for hyperspectral video tracking, which is capable of utilizing spectral–spatial features and considering global interactions. Specifically, the feature extraction module employs two parallel branches to extract multiple-level coarse-grained and fine-grained spectral–spatial features, which are fused with adaptive weights. The extracted features are further fused with the context fusion module based on a transformer with the hyperspectral self-attention (HSA) and hyperspectral cross-attention (HCA), which are designed to capture the self-context feature interaction and the cross-context feature interaction, respectively. Furthermore, an adaptive dynamic template updating strategy is used to update the template bounding box based on the prediction score. The extensive experimental results on benchmark hyperspectral video tracking datasets demonstrated that the proposed SSTFT outperforms the state-of-the-art methods in both precision and speed. [ABSTRACT FROM AUTHOR]

Semantic segmentation of high-spatial-resolution (HSR) remote sensing (RS) images has been extensively studied, and most of the existing methods are based on convolutional neural network (CNN) models. However, the CNN is regarded to have less power in global representation modeling. In the past few years, methods using transformer have attracted increasing attention and generate improved results in semantic segmentation of natural images, owing to their powerful ability in global information acquisition. Nevertheless, these transformer-based methods exhibit limited performance in semantic segmentation of RS images, probably because of the lack of comprehensive understanding in the feature decoding process. In this paper, a novel transformer-based model named the bi-decoder transformer segmentor for remote sensing (BiTSRS) is proposed, aiming at alleviating the problem of flexible feature decoding, through a bi-decoder design for semantic segmentation of RS images. In the proposed BiTSRS, the Swin transformer is adopted as encoder to take both global and local representations into consideration, and a unique design module (ITM) is designed to deal with the limitation of input size for Swin transformer. Furthermore, BiTSRS adopts a bi-decoder structure consisting of a Dilated-Uper decoder and a fully deformable convolutional network (FDCN) module embedded with focal loss, with which it is capable of decoding a wide range of features and local detail deformations. Both ablation experiments and comparison experiments were conducted on three representative RS images datasets. The ablation analysis demonstrates the contributions of specifically designed modules in the proposed BiTSRS to performance improvement. The comparison experimental results illustrate that the proposed BiTSRS clearly outperforms some state-of-the-art semantic segmentation methods. [ABSTRACT FROM AUTHOR]

Remote-vision-based image processing plays a vital role in the safety helmet and harness monitoring of construction sites, in which computer-vision-based automatic safety helmet and harness monitoring systems have attracted significant attention for practical applications. However, many problems have not been well solved in existing computer-vision-based systems, such as the shortage of safety helmet and harness monitoring datasets and the low accuracy of the detection algorithms. To address these issues, an attribute-knowledge-modeling-based safety helmet and harness monitoring system is constructed in this paper, which elegantly transforms safety state recognition into images' semantic attribute recognition. Specifically, a novel transformer-based end-to-end network with a self-attention mechanism is proposed to improve attribute recognition performance by making full use of the correlations between image features and semantic attributes, based on which a security recognition system is constructed by integrating detection, tracking, and attribute recognition. Experimental results for safety helmet and harness detection demonstrate that the accuracy and robustness of the proposed transformer-based attribute recognition algorithm obviously outperforms the state-of-the-art algorithms, and the presented system is robust to challenges such as pose variation, occlusion, and a cluttered background. [ABSTRACT FROM AUTHOR]

Spatial context has been demonstrated to be effective to constrain sparse unmixing (SU) of hyperspectral images. However, the existing algorithms employed simple spatial information without keeping spectral fidelity. By considering the fact that adjacent pixels own not only the endmembers with same variations but also approximated fractional abundances, in this paper, local spectral similarity preserving (LSSP) constraint is proposed to preserve spectral similarity in a local area during robust sparse unmixing (RSU). Specially, four LSSP constraints are constructed using different-norm-constrained pixel-level difference over abundance-level difference in a local area. Moreover, a convex optimization algorithm is proposed to solve the proposed LSSP-constrained RSU (LSSP-RSU). Experimental results on both synthetic and real hyperspectral data demonstrate that the developed algorithms yield better values of the signal-to-reconstruction error (SRE). Especially, when using $l_{2}$ norm of pixel-level difference to weight the $l_{1}$ norm of abundance-level difference, the proposed LSSP-RSU algorithm can achieve superior unmixing performance. [ABSTRACT FROM AUTHOR]

The explosive growth of video data constitutes a series of new challenges in computer vision, and the function of video summarization (VS) is becoming more and more prominent. Recent works have shown the effectiveness of sparse dictionary selection (SDS) based VS, which selects a representative frame set to sufficiently reconstruct a given video. Existing SDS based VS methods use conventional handcrafted features or single-scale deep features, which could diminish their summarization performance due to the underutilization of frame feature representation. Deep learning techniques based on convolutional neural networks (CNNs) exhibit powerful capabilities among various vision tasks, as the CNN provides excellent feature representation. Therefore, in this paper, a multi-scale deep feature fusion based sparse dictionary selection (MSDFF-SDS) is proposed for VS. Specifically, multi-scale features include the directly extracted features from the last fully connected layer and the global average pooling (GAP) processed features from intermediate layers, then VS is formulated as a problem of minimizing the reconstruction error using the multi-scale deep feature fusion. In our formulation, the contribution of each scale of features can be adjusted by a balance parameter, and the row-sparsity consistency of the simultaneous reconstruction coefficient is used to select as few keyframes as possible. The resulting MSDFF-SDS model is solved by using an efficient greedy pursuit algorithm. Experimental results on two benchmark datasets demonstrate that the proposed MSDFF-SDS improves the F-score of keyframe based summarization more than 3% compared with the existing SDS methods, and performs better than most deep-learning methods for skimming based summarization. • Usage of multi-scale features from neural networks for video summarization. • Multi-scale deep feature fusion based sparse dictionary selection. • Efficient greedy optimization for video summarization. • Explorations of feature configurations, network architectures, and pooling strategies. [ABSTRACT FROM AUTHOR]

To solve the problem that traditional dark channel is not suitable for a large sky area and can easyily distort defogged images, we propose a novel fusion-based defogging algorithm. Firstly, an improved remote sensing image segmentation algorithm is introduced to mix the dark channel. Secondly, we establish a dark-light channel fusion model to calculate the atmospheric light map. Furthermore, in order to refine the transmittance image without reducing restoration quality, the grayscale image corresponding to the original image is selected as a guide image. Meanwhile, we optimize the set value of the defogging intensity parameter ω in the transmission estimation formula as the value of atmospheric light. Finally, a brightness/color compensation model based on visual perception is generated for image correction. Experimental results demonstrate that the proposed algorithm achieves superior performance on UAV foggy images in both subjective and objective evaluation, which verifies the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Due to its excellent performance in high-dimensional space, the kernel extreme learning machine has been widely used in pattern recognition and machine learning fields. In this paper, we propose a dual-weighted kernel extreme learning machine for hyperspectral imagery classification. First, diverse spatial features are extracted by guided filtering. Then, the spatial features and spectral features are composited by a weighted kernel summation form. Finally, the weighted extreme learning machine is employed for the hyperspectral imagery classification task. This dual-weighted framework guarantees that the subtle spatial features are extracted, while the importance of minority samples is emphasized. Experiments carried on three public data sets demonstrate that the proposed dual-weighted kernel extreme learning machine (DW-KELM) performs better than other kernel methods, in terms of accuracy of classification, and can achieve satisfactory results. [ABSTRACT FROM AUTHOR]