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Building information serves as a critical foundational dataset in the fields of urban planning, smart cities and surveying and mapping, and high-resolution remote sensing (HRRS) imagery has become a vital data source for extracting building information. However, automatically extracting building information from HRRS imagery using a single feature or method remains a challenging task. On one hand, buildings exhibit significant variations in terms of their size, color, geometric structures, and other aspects. On the other hand, there are also numerous features in the environment that bear spectral and morphological resemblances to buildings. In this article, we proposed a multifeature fusion framework based on Dempster–Shafer (D-S) theory that consists of two steps for automated building extraction from HRRS imagery. The initial D-S fusion step involves two branches: object-level and pixel-level feature fusion. Then the outcomes are further combined to derive the ultimate building confidence information. In the framework, we introduced a proportional consistency and centroid consistency index to convert pixel-level features to object-level features, thereby facilitating their fusion. In addition, we proposed an initialization module for the basic probability assignment formula, enabling the elimination of the impact of nonbuilding objects and simplifying the construction process of BPAF. The experimental results based on the Nanjing, WHU, and Washington datasets demonstrate the effectiveness of our method, the accuracy outperforms the other four advanced algorithms.

Single-image rain removal is an extremely challenging task as it requires not only removing rain streaks with complex shapes, scales, and opacities but also recovering spatial details and high-level contextual structures of the underlying image. Although deep learning networks have achieved encouraging performance, current research mainly focuses on building deeper and more complex network architectures to recover reliable detailed textures or utilizing multi-scale encoder-decoder structures to learn semantic contexts in larger receptive fields, they are still not sufficient to balance rain streak removal and detail preservation. In this study, we propose a novel end-to-end network, called a dual-task complementary network (DTCN), composed of a detail recovery progressive network (DRPN) and a multifeature fusion encoder-decoder network (MEDN), to balance rain streak removal and detail preservation. Specifically, DRPN is designed to recover details in the original image, while MEDN is used to remove structural rain streaks. In addition, to reconstruct more natural and clear images, we integrate multiple network training losses, including structural similarity loss, perceptual contrast loss, perceptual image similarity loss, and edge loss. Experimental results demonstrate that our method outperforms several state-of-the-art methods on both synthetic and real rainy images. The code will be uploaded to https://github.com/zhang152267/DTCN. [ABSTRACT FROM AUTHOR]

Summary: As an important technology in computer vision, video‐based human action recognition has a great commercial value, which has attracted extensive attention in the field of computer vision and pattern recognition in both academia and industry. To date, there are a wide variety of applications of human action recognition, such as surveillance, robotics, health care, video searching, and human–computer interaction. However, there are many challenges involved in human action recognition in videos, such as cluttered backgrounds, occlusions, viewpoint variation, execution rate, and camera motion. However, data redundancy and single feature were largely limited the accuracy of human action recognition. In this article, adopting the key frame extraction and multifeature fusion techniques, a novel action recognition method was proposed, which can improve the recognition accuracy. The main works are as follows: 1) in order to solve the problem of data redundancy, a key frame extraction method based on node contribution weighting is proposed to extract video key frames; 2) different kinds of information flows are extracted from the obtained key frame sequences, and different convolutional neural networks are used to obtain corresponding classification results and merge, so as to better complement the information in different flows. Lastly, the experimental results show that our method improves the accuracy of action recognition. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The non-blind deblurring approach can adequately deblur single-blur images by applying a suitable mathematical model. In contrast, it cannot satisfactorily deblur images that have multiple blurs. The blind deblurring approach is able to remove various kinds of blurs from an image. However, because the causes of blur in different regions differ, it is difficult to locate and remove all the blurs accurately and also to recover the fine texture details. Considering these weaknesses and strengths of both approaches, we propose a neural network that dynamically selects suitable blur kernels for deblurring. In the proposed method, the most appropriate kernels are extracted by joint training from multiple datasets that contain specific types of blurs to tackle local and global regions in one image. In addition, to further improve the image restoration quality, we designed an edge-attention mechanism to compensate the edges and structures of specific objects. The results of experiments conducted indicate that the dynamic selection of blur kernels combined with the edge attention algorithm not only improves PSNR and SSIM, but also outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the advancement of Internet of Things (IoT) and artificial intelligence technologies, and the need for rapid application growth in fields, such as security entrance control and financial business trade, facial information processing has become an important means for achieving identity authentication and information security. However, in the process of acquiring facial feature information, face information is easily affected by factors, such as object occlusion, lighting changes, and similar backgrounds. In this paper, we propose a multifeature fusion algorithm based on integral histograms and a real‐time update tracking particle filtering (PF) module. First, edge features and colour features are extracted, weighting methods are used to weight the colour histogram and edge features to describe facial features, and fusion of colour features and edge features is made adaptive by using fusion coefficients to improve face tracking reliability. Then, the integral histogram is integrated into the PF algorithm to simplify the calculation steps of complex particles and improve operational efficiency. Finally, the tracking window size is adjusted in real‐time according to the change in the average distance from the particle centre to the edge of the current model and the initial model to reduce the drift problem and achieve stable tracking with significant changes in the target dimension. The results show that the algorithm improves video tracking accuracy, simplifies particle operation complexity, improves the speed, and has good anti‐interference ability and robustness compared with extracting a single feature. [ABSTRACT FROM AUTHOR]

Multispectral remote sensing images are widely used for monitoring the globe. Although thin clouds can affect all optical bands, the influences of thin clouds differ with band wavelength. When processing multispectral bands at different resolutions, many methods only remove thin clouds in visible/near-infrared bands or rescale multiresolution bands to the same resolution and then process them together. The former cannot make full use of multispectral information, and in the latter, the rescaling process will introduce noise. In this article, a deep-learning-based thin cloud removal method that fuses full spectral and spatial features in original Sentinel-2 bands is proposed, named CR4S2. A multi-input and output architecture is designed for better fusing information in all bands and reconstructing the background at original resolutions. In addition, two parallel downsampling residual blocks are designed to transfer features extracted from different depths to the bottom of the network. Experiments were conducted on a new globally distributed Sentinel-2 thin cloud removal dataset called WHUS2-CRv. The results show that the best averaged peak signal-to-noise ratio, structural similarity index measurement, normalized root-mean-square error, and spectral angle mapper of the proposed method over 12 bands in all 20 testing images were 39.55, 0.9443, 0.0245, and 2.5676°, respectively. Compared with baseline methods, the proposed CR4S2 method can better restore not only the spatial features but also spectral features. This indicates that the proposed method is very promising for removing thin clouds in multispectral remote sensing images at different resolutions.

Copyright of Journal of Zhejiang University: Science A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recently due to the good balance between performance and tracking speed, the discriminative correlation filter (DCF) has become a popular and excellent tracking method in short-term tracking. Computing the correlation of a response map can be efficiently performed in the Fourier domain by the discrete Fourier transform (DFT) of the input, where the DFT of an image has symmetry in the Fourier domain. However, most of the correlation filter (CF)-based trackers cannot deal with the tracking results and lack the effective mechanism to adjust the tracked errors during the tracking process, thus usually perform poorly in long-term tracking. In this paper, we propose a long-term tracking framework, which includes a tracking-by-detection part and redetection part. The tracking-by-detection part is built on a DCF framework, by integrating with a multifeature fusion model, which can effectively improve the discriminant ability of the correlation filter for some challenging situations, such as occlusion and color change. The redetection part can search the tracked object in a larger region and refine the tracking results after the tracking has failed. Benefited by the proposed redetection strategy, the tracking results are re-evaluated and refined, if it is necessary, in each frame. Moreover, the reliable estimation module in the redetection part can effectively identify whether the tracking results are correct and determine whether the redetector needs to open. The proposed redetection part utilizes a saliency detection algorithm, which is fast and valid for object detection in a limited region. These two parts can be integrated into DCF-based tracking methods to improve the long-term tracking performance and robustness. Extensive experiments on OTB2015 and VOT2016 benchmarks show that our proposed long-term tracking method has a proven effectiveness and high efficiency compared with various tracking methods. [ABSTRACT FROM AUTHOR]

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• A multifeature fusion model was proposed for online surface roughness measurement of cold-rolled strip steel. • The statistical and depth features of speckle images were extracted to evaluate the surface roughness of cold-rolled strip steel. • Convolutional neural networks and an attention mechanism module were adopted to extract the depth features of the speckle images. • A support vector regression algorithm was used to predict the surface roughness. The online measurement of the surface roughness of cold-rolled strip steel plays a significant role in the steel manufacturing process. However, the traditional mechanism method based on laser speckle is not sufficient for image processing, and the precision of online measurement is not high. Therefore, this paper proposes a multifeature fusion model for the surface roughness of cold-rolled strip steel to improve the measurement efficiency of existing speckle methods. Cold-rolled strip steel is irradiated by a laser beam to produce speckle images. Statistical features are extracted using a gray-level cooccurrence matrix (GLCM). The deep information of an image is extracted using convolutional neural networks (CNN) and a convolutional block attention mechanism module (CBAM). The features extracted from the GLCM and CNN-CBAM methods are combined to create a multifeature fusion dataset. The fused features are predicted by a support vector regression (SVR) algorithm. Then, the SVR model is compared with three machine learning prediction models: classification and regression tree (CART), random forest (RF), and k-nearest neighbor (KNN). Experimental results confirm that the proposed multifeature fusion model predicts surface roughness with a minimum mean square error of 0.237%. [ABSTRACT FROM AUTHOR]

Recently due to the good balance between performance and tracking speed, the discriminative correlation filter (DCF) has become a popular and excellent tracking method in short-term tracking. Computing the correlation of a response map can be efficiently performed in the Fourier domain by the discrete Fourier transform (DFT) of the input, where the DFT of an image has symmetry in the Fourier domain. However, most of the correlation filter (CF)-based trackers cannot deal with the tracking results and lack the effective mechanism to adjust the tracked errors during the tracking process, thus usually perform poorly in long-term tracking. In this paper, we propose a long-term tracking framework, which includes a tracking-by-detection part and redetection part. The tracking-by-detection part is built on a DCF framework, by integrating with a multifeature fusion model, which can effectively improve the discriminant ability of the correlation filter for some challenging situations, such as occlusion and color change. The redetection part can search the tracked object in a larger region and refine the tracking results after the tracking has failed. Benefited by the proposed redetection strategy, the tracking results are re-evaluated and refined, if it is necessary, in each frame. Moreover, the reliable estimation module in the redetection part can effectively identify whether the tracking results are correct and determine whether the redetector needs to open. The proposed redetection part utilizes a saliency detection algorithm, which is fast and valid for object detection in a limited region. These two parts can be integrated into DCF-based tracking methods to improve the long-term tracking performance and robustness. Extensive experiments on OTB2015 and VOT2016 benchmarks show that our proposed long-term tracking method has a proven effectiveness and high efficiency compared with various tracking methods.

The selection and representation of remote sensing image classification features play crucial roles in image classification accuracy. To effectively improve the classification accuracy of features, an improved U-Net network framework based on multi-feature fusion perception is proposed in this paper. This framework adds the channel attention module (CAM-UNet) to the original U-Net framework and cascades the shallow features with the deep semantic features, replaces the classification layer in the original U-Net network with a support vector machine, and finally uses the majority voting game theory algorithm to fuse the multifeature classification results and obtain the final classification results. This study used the forest distribution in Xingbin District, Laibin City, Guangxi Zhuang Autonomous Region as the research object, which is based on Landsat 8 multispectral remote sensing images, and, by combining spectral features, spatial features, and advanced semantic features, overcame the influence of the reduction in spatial resolution that occurs with the deepening of the network on the classification results. The experimental results showed that the improved algorithm can improve classification accuracy. Before the improvement, the overall segmentation accuracy and segmentation accuracy of the forestland increased from 90.50% to 92.82% and from 95.66% to 97.16%, respectively. The forest cover results obtained by the algorithm proposed in this paper can be used as input data for regional ecological models, which is conducive to the development of accurate and real-time vegetation growth change models.

The selection and representation of remote sensing image classification features play crucial roles in image classification accuracy. To effectively improve the classification accuracy of features, an improved U-Net network framework based on multi-feature fusion perception is proposed in this paper. This framework adds the channel attention module (CAM-UNet) to the original U-Net framework and cascades the shallow features with the deep semantic features, replaces the classification layer in the original U-Net network with a support vector machine, and finally uses the majority voting game theory algorithm to fuse the multifeature classification results and obtain the final classification results. This study used the forest distribution in Xingbin District, Laibin City, Guangxi Zhuang Autonomous Region as the research object, which is based on Landsat 8 multispectral remote sensing images, and, by combining spectral features, spatial features, and advanced semantic features, overcame the influence of the reduction in spatial resolution that occurs with the deepening of the network on the classification results. The experimental results showed that the improved algorithm can improve classification accuracy. Before the improvement, the overall segmentation accuracy and segmentation accuracy of the forestland increased from 90.50% to 92.82% and from 95.66% to 97.16%, respectively. The forest cover results obtained by the algorithm proposed in this paper can be used as input data for regional ecological models, which is conducive to the development of accurate and real-time vegetation growth change models. [ABSTRACT FROM AUTHOR]