With the improvement of the quality and resolution of remote sensing (RS) images, scene recognition tasks have played an important role in the RS community. However, due to the special bird’s eye view image acquisition mode of imaging sensors, it is still challenging to construct a discriminate representation of diverse and complex scenes to improve RS image recognition performance. Capsule networks that can learn the spatial relationship between the features in an image has a good image classification performance. However, the original capsule network is not suitable for images with a complex background. To address the above issues, this paper proposes a novel end-to-end capsule network termed DS-CapsNet, in which a new multi-scale feature enhancement module and a new Caps-SoftPool method are advanced by aggregating the advantageous attributes of the residual convolution architecture, Diverse Branch Block (DBB), Squeeze and Excitation (SE) block, and the Caps-SoftPool method. By using the residual DBB, multiscale features can be extracted and fused to recover a semantic strong feature representation. By adopting SE, the informative features are emphasized, and the less salient features are weakened. The new Caps-SoftPool method can reduce the number of parameters that are needed in order to prevent an over-fitting problem. The novel DS-CapsNet achieves a competitive and promising performance for RS image recognition by using high-quality and robust capsule representation. The extensive experiments on two challenging datasets, AID and NWPU-RESISC45, demonstrate the robustness and superiority of the proposed DS-CapsNet in scene recognition tasks.

Object detection and analysis in remote sensing images is a critical research subject for many businesses and agencies. At present, object detection based on convolutional neural network (CNN) in natural scenes has good performance. Due to the large number of small objects and similar characteristics between the objects in the VisDrone dataset, the current model cannot extract more small-scale features. Therefore, this paper proposes a stronger feature extraction FasterRCNN (SFE-FasterRCNN) that advances a feature extraction strengthening network to enhance the feature learning ability for different objects. Specifically, the pixel proposal network (PPN) is proposed by combining the low-resolution and strong semantic features with high-resolution and weak semantic features through a top-down approach and reusing these fusion blocks vertically to construct a comprehensive semantic feature map. Then hyperbolic pooling is proposed to minimize the loss of feature information during the activation mapping process. Finally, data clustering is used to adaptively generate better object proposals according to the characteristics of the dataset. Experimental results on the VisDrone dataset show that our method has excellent detection results.

Many exceptional deep learning networks have demonstrated remarkable proficiency in general object detection tasks. However, the challenge of detecting ships in synthetic aperture radar (SAR) imagery increases due to the complex and various nature of these scenes. Moreover, sophisticated large-scale models necessitate substantial computational resources and hardware expenses. To address these issues, a new framework is proposed called a stepwise attention-guided multiscale feature fusion network (SAFN). Specifically, we introduce a stepwise attention mechanism designed to selectively emphasize relevant information and filter out irrelevant details of objects in a step-by-step manner. Firstly, a novel LGA-FasterNet is proposed, which incorporates a lightweight backbone FasterNet with lightweight global attention (LGA) to realize expressive feature extraction while reducing the model’s parameters. To effectively mitigate the impact of scale and complex background variations, a deformable attention bidirectional fusion network (DA-BFNet) is proposed, which introduces a novel deformable location attention (DLA) block and a novel deformable recognition attention (DRA) block, strategically integrating through bidirectional connections to achieve enhanced features fusion. Finally, we have substantiated the robustness of the new framework through extensive testing on the publicly accessible SAR datasets, HRSID and SSDD. The experimental outcomes demonstrate the competitive performance of our approach, showing a significant enhancement in ship detection accuracy compared to some state-of-the-art methods.

With the rapid development of electric vehicles (EVs), the safety of battery systems has become an increasing concern. However, the application of multi-objective optimization algorithms in the safety domain of EVs is rare, particularly in optimizing complex, non-linear multi-objective problems (MOPs) such as crashworthiness and thermal management. This work proposes an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D), which introduces a dynamic grid system for building stability matrices to determine whether the current population reaches a stable convergence state with less computational demands and designs a novel sparsity level evaluation to measure the weight vectors. Additionally, we propose a multiple dominance to address the Pareto dominance limitation. These aim to optimize the key issues in EV safety. To further demonstrate the performance of our proposed algorithm, we compare it with 10 state-of-the-art algorithms on 26 benchmark problems. The experimental results based on multiple performance metrics show that the proposed algorithm have outstanding performance.

Conventional correction approaches are unsuitable for effectively correcting remote sensing images acquired in the seriously oblique condition which has severe distortions and resolution disparity. Considering that the extraction of control points (CPs) and the parameter estimation of the correction model play important roles in correction accuracy, this paper introduces an effective correction method for large angle (LA) images. Firstly, a new CP extraction algorithm is proposed based on multi-view simulation (MVS) to ensure the effective matching of CP pairs between the reference image and the LA image. Then, a new piecewise correction algorithm is advanced with the optimized CPs, where a concept of distribution measurement (DM) is introduced to quantify the CPs distribution. The whole image is partitioned into contiguous subparts which are corrected by different correction formulae to guarantee the accuracy of each subpart. The extensive experimental results demonstrate that the proposed method significantly outperforms conventional approaches.

Hail poses a significant meteorological hazard in China, leading to substantial economic and agricultural damage. To enhance the detection of hail and mitigate these impacts, this study presents an ensemble machine learning model (BPNN+Dtree) that combines a backpropagation neural network (BPNN) and a decision tree (Dtree). Using FY-4A satellite and ERA5 reanalysis data, the model is trained on geostationary satellite infrared data and environmental parameters, offering comprehensive, all-day, and large-area hail monitoring over China. The ReliefF method is employed to select 13 key features from 29 physical quantities, emphasizing cloud-top and thermodynamic properties over dynamic ones as input features for the model to enhance its hail differentiation capability. The BPNN+Dtree ensemble model harnesses the strengths of both algorithms, improving the probability of detection (POD) to 0.69 while maintaining a reasonable false alarm ratio (FAR) on the test set. Moreover, the model’s spatial distribution of hail probability more closely matches the observational data, outperforming the individual BPNN and Dtree models. Furthermore, it demonstrates improved regional applicability over overshooting top (OT)-based methods in the China region. The identified high-frequency hail areas correspond to the north-south movement of the monsoon rain belt and are consistent with the northeast-southwest belt distribution observed using microwave-based methods.