In recent years, general object detection on nature images leverages convolutional neural networks (CNNs) and vision transformers (ViT) to achieve great progress and high accuracy. However, unlike nature scenarios, remote sensing systems usually deploy a large number of edge devices. Therefore, this condition encourages detectors to have lower parameters than high-complexity neural networks. To this end, we propose an effective and lightweight detection framework of hybrid network, which enhances representation learning to balance efficiency and accuracy of model. Specifically, to compensate low precision caused by lightweight neural networks, we design a boundary-aware context (BAC) module and a frequency self-attention refinement (FSAR) module to improve detector performance in a hybrid structure. The BAC module enhances the local features of the object by fusing the spatial context information with the original image, which not only improves the model accuracy but also effectively solves the problem of multiscale objects. To alleviate the interference of complex background, the FSAR module adopts an adaptive technology to filter out redundant information at different frequencies to improve overall detection performance. The comprehensive experiments on remote sensing datasets, i.e., NWPU VHR-10, LEVIR, and RSOD, indicate that the proposed method achieves state-of-the-art performance and balances between model size and accuracy.

Due to resource constraints on edge devices, lightweight detection models are gaining popularity in remote sensing. However, achieving efficient performance with these models is challenging compared to traditional detection models. Knowledge distillation (KD) is a promising solution to this issue. However, previous KD methods in remote sensing often suffer from background noise and fail to address feature disparities among detectors. To address the above issues, we introduce the Two-Way Assistant (TWA) distillation method for remote sensing object detection. TWA comprises two crucial modules: the Compression Assistant Module (CPAM) and the Multiscale Adaptive Assistant Module (MAAM). CPAM reduces background information and category interference by compressing and redistributing teacher model features to the student model. MAAM enhances feature knowledge through multi-scale fusion, addressing feature disparities. Through extensive experiments conducted on two distinct types of remote sensing datasets, optical LEVIR and SAR SSDD, our TWA demonstrates favourable performance across both single-stage and two-stage detectors. Especially, it achieves a performance of 82.5% (optical LEVIR dataset) and 95.4% (SAR SSDD dataset) in the AP ₅₀ metric, superior to existing state-of-the-art methods.

In order to improve the acquisition and recommendation effect of English education resources, this paper proposes a Rank algorithm of web English educational resources based on fuzzy sets and RSS, and deeply studies the basic principles of the algorithm and introduces several keyword extraction techniques. The user’s browsing behavior and user interest acquisition methods are classified. Researchers can plan to further explore the page ranking algorithm to improve the performance of the scheme based on the damping factor. In addition, this paper uses Web technology to acquire English education resources and build a recommendation model, and uses crawler technology to build an overall system model. Finally, this paper designs experiments to verify the performance of the algorithm model constructed in this paper, and analyses the experimental results by mathematical statistics. The research results show that the algorithm model proposed in this paper has significant effects and is of great significance to the acquisition and recommendation of English education resources.

Camera failure or loss of storage components in imaging equipment may result in the loss of important image information or random pulse noise interference. The low-rank prior is one of the most important priors in image optimization processing. This paper reviews and compares some low-rank constraint models for image matrices. Firstly, an overview of image-inpainting models based on nuclear norm, truncated nuclear norm, weighted nuclear norm, and matrix-factorization-based F norm is presented, and corresponding optimization iterative algorithms are provided. Then, we use different image matrix low-order constraint models to recover satellite images from three types of pulse interference and provide our experimental visual and numerical results. Finally, it can be concluded that the method based on the weighted nuclear norm can achieve the best image restoration effect. The F norm method based on matrix factorization has the shortest computational time and can be used for large-scale low-rank matrix calculations. Compared with nuclear norm-based methods, weighted nuclear norm-based methods and truncated nuclear norm-based methods can significantly improve repair performance.

Camera malfunctions or loss of storage elements in imaging devices may lead to loss of important image information or random impulse noise interference. Low rank is one of the important prior information in image optimization processing. This paper uses different low-rank constraint models of the image matrices to recover the impulse interference satellite images. Firstly, an overview of image inpainting models based on nuclear norm, truncated nuclear norm, weighted nuclear norm, and matrix-factorization-based F-norm is presented, and corresponding optimiza-tion iterative algorithms are provided. Then, we conducted experiments under three types of pulse interference and provided visual and numerical results. Finally, it is concluded that the WSVT_ADMM method based on the weighted nuclear norm can obtain the best image inpainting results; The UV_ADMM method based on F norm of matrix factorization has the least computa-tion time and can be used for large-scale low-rank matrix computation; The WSVT_ADMM method based on weighted nuclear norm and the TSVT_ ADMM method based on truncated nuclear norms can significantly improve the repair effect compared to the nuclear norm-based methods such as SVT, SVP, and n_ADMM.

Employing low tensor rank decompositions in image inpainting has attracted increasing attention. This paper exploits a novel tensor-augmentation schemes to transform an image (a low-order tensor) to a higher-order tensor without changing the total number of pixels. The developed augmentation schemes enhance the low-rankness of an image under three tensor decompositions: matrix SVD, tensor train (TT) decomposition, and tensor singular value decomposition (t-SVD). By exploiting the schemes, we solve the image inpainting problem with three low-rank con-strained models which use the matrix rank, TT rank, and tubal rank as constrained priors re-spectively. The tensor tubal rank and tensor train multi-rank are developed from t-SVD and TT decomposition respectively. We exploit efficient ADMM algorithms for solving the three models. Experimental results demonstrate that our methods are effective for image inpainting and supe-rior to numerous close methods.