With the recent development of the joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data, deep learning methods have achieved promising performance owing to their locally sematic feature extracting ability. Nonetheless, the limited receptive field restricted the convolutional neural networks (CNNs) to represent global contextual and sequential attributes, while visual image transformers (VITs) lose local semantic information. Focusing on these issues, we propose a fractional Fourier image transformer (FrIT) as a backbone network to extract both global and local contexts effectively. In the proposed FrIT framework, HSI and LiDAR data are first fused at the pixel level, and both multisource feature and HSI feature extractors are utilized to capture local contexts. Then, a plug-and-play image transformer FrIT is explored for global contextual and sequential feature extraction. Unlike the attention-based representations in classic VIT, FrIT is capable of speeding up the transformer architectures massively and learning valuable contextual information effectively and efficiently. More significantly, to reduce redundancy and loss of information from shallow to deep layers, FrIT is devised to connect contextual features in multiple fractional domains. Five HSI and LiDAR scenes including one newly labeled benchmark are utilized for extensive experiments, showing improvement over both CNNs and VITs.

Medical image fusion (MIF) has received painstaking attention due to its diverse medical applications in response to accurately diagnosing clinical images. Numerous MIF methods have been proposed to date, but the fused image suffers from poor contrast, non-uniform illumination, noise presence, and improper fusion strategies, resulting in an inadequate sparse representation of significant features. This paper proposes the morphological preprocessing method to address the non-uniform illumination and noise by the bottom-hat–top-hat strategy. Then, grey-principal component analysis (grey-PCA) is used to transform RGB images into gray images that can preserve detailed features. After that, the local shift-invariant shearlet transform (LSIST) method decomposes the images into the low-pass (LP) and high-pass (HP) sub-bands, efficiently restoring all significant characteristics in various scales and directions. The HP sub-bands are fed to two branches of the Siamese convolutional neural network (CNN) by process of feature detection, initial segmentation, and consistency verification to effectively capture smooth edges, and textures. While the LP sub-bands are fused by employing local energy fusion using the averaging and selection mode to restore the energy information. The proposed method is validated by subjective and objective quality assessments. The subjective evaluation is conducted by a user case study in which twelve field specialists verified the superiority of the proposed method based on precise details, image contrast, noise in the fused image, and no loss of information. The supremacy of the proposed method is further justified by obtaining 0.6836 to 0.8794, 0.5234 to 0.6710, and 3.8501 to 8.7937 gain for ${\mathrm{Q}}_{\mathrm{F}}^{\mathrm{AB}}$, CRR, and AG and noise reduction from 0.3397 to 0.1209 over other methods for objective parameters.

To further improve the performance of linear extended state observer (LESO), a novel fractional-order linear extended state observer (FOLESO) is proposed in this paper. By incorporating a fractional-order term into the framework of traditional LESO, the design of FOLESO is more flexible and thereby the disturbance estimation error can be further reduced. Based on the proposed FOLESO, a fractional-order linear active disturbance rejection control (FOLADRC) strategy has been designed for grid-side current control of three-phase voltage-type pulse width modulation (PWM) rectifiers. In this way, the harmonic suppression property can be enhanced without sacrificing the tracking performance, and the ideal grid-side current can be achieved. Furthermore, the detailed design process, implementation mode and parameter tuning of FOLADRC are also given in this paper. The theoretical analysis and experimental results show that the proposed method is effective and superior to the traditional PI control or linear active disturbance rejection control (LADRC) strategy.

Deep learning (DL) has achieved significant attention in the field of infrared (IR) and visible (VI) image fusion, and several attempts have been made to enhance the quality of the final fused image. It produces better results than conventional methods; however, the captured image cannot acquire useful information due to environments with poor lighting, fog, dense smoke, haze, and the noise generated by sensors. This paper proposes an adaptive fuzzy-based preprocessing enhancement method that automatically enhances the contrast of images with adaptive parameter calculation. The enhanced images are then decomposed into base and detail layers by anisotropic diffusion-based edge-preserving filters that remove noise while smoothing the edges. The detailed parts are fed into four convolutional layers of the VGG-19 network through transfer learning to extract features maps. These features maps are fused by multiple fusion strategies to obtain the final fused detailed layer. The base parts are fused by the PCA method to preserve the energy information. Experimental results reveal that our proposed method achieves state-of-the-art performance compared with existing fusion methods in a subjective evaluation through the visual experience of experts and statistical tests. Moreover, the objective assessment parameters are conducted by various parameters ($FMI$, $SSI{M}_a$, $API$, $\mathrm{EN}$, ${\mathrm{Q}}_{\mathrm{F}}^{\mathrm{AB}}$, and $N_F^{AB}$) which were used in the comparison method. The proposed method achieves 0.2651 to 0.3951, 0.5827 to 0.8469, 56.3710 to 71.9081, 4.0117 to 7.9907, and 0.6538 to 0.8727 gain for $FMI$, $SSI{M}_a$, $API$, $\mathrm{EN}$, and ${\mathrm{Q}}_{\mathrm{F}}^{\mathrm{AB}}$, respectively. At the same time, the proposed method has more noise reduction (0.3049 to 0.0021) that further justifies the efficacy of the proposed method than conventional methods.

Measuring the temperature of insulator in infrared image and diagnosing whether there is abnormal temperature rise is important for electric power inspection. Traditional object detection uses horizontal detection boxes. When they are applied to insulator temperature measurement, their rough positioning results in measurement deviation. Although some detectors with rotated box have been used to tackle this problem, their rotation angle prediction is still not precise enough, resulting in the positioning deviation of insulator core rod, which also leads to temperature measurement deviation. In this paper, we propose a complete solution to address these problems. First, we design an adaptive balanced feature pyramid network (Adaptive BFPN) to fuse localization and semantic information more efficiently, which can improve the detection integrity of large-scale insulators. Second, we design a novel θ-Intersection over Union (θ-IoU) loss to enhance the accuracy of the detector in predicting rotation angle and insulator positioning. Third, we propose a method to locate and automatically correct the position of insulator core rod, measure the temperature, and diagnose defects. It enables faster temperature measurement and more automatic defect diagnosis. Finally, comparative experiments show that our method has a higher accuracy in diagnosing overheating defects than other advanced methods, reaching 97.07%. Our method provides a more intelligent and automated solution for inspecting insulator overheating defects.

Vision-based Marine ship detection has been explored for many years, effectively improving maritime transportation management. However, many ship detection methods get troubles on the movable platform, which are summarized as follows: 1) high time efficiency is needed to handle the rapid changing of maritime scenes so that ships can be detected successfully on movable platform; 2) different appearances caused by sizes and viewpoints enlarge the intra-class distance of ships, which exceed the representation capability of some features like histogram of oriented gradients (HOG) with fixed size. For addressing these issues, we propose a rapid ship detection method based on multi-size gradient features and multi-branch support vector machine (SVM) in a ``coarse-to-fine'' manner that can be applied on a movable platform. The proposed multi-size gradient features are used to represent the ships with different sizes, including the coarse and fine gradient features. To speed up the detection process in a sliding way, the coarse ship locations are firstly generated only based on the coarse gradient features, which highly reduces the computational cost. Then, the multi-size gradient features extracted from these locations are determined by a multi-branch SVM model which is designed to deal with features of different dimensions and improve the precision of ship position with fine gradient features. The proposed method is tested on the changing background where ships have different sizes. Experimental results show that the proposed method obtains average precision (AP) of 0.795 and its detection speed achieves 60.6 frame per second, which achieve real-time performance and satisfactory detection precision.