Remote-field eddy current (RFEC) inspection has shown its advantages over the other techniques in Electromagnetic non-destructive evaluation (ENDE) techniques in detection and characterization of deeper defects. In this paper, a new RFEC transducer has been designed for inspection of flat conductive plates, based on numerical simulations using finite element method. Through experimental study, it is concluded that the new transducer has the capability of detecting deep buried defects, and that the phase of RFEC signals have linear relation with the depth of the defects.

Synthetic aperture radar (SAR) image ship detection is currently a research hotspot in the field of national defense science and technology. However, SAR images contain a large amount of coherent speckle noise, which poses significant challenges in the task of ship detection. To address this issue, we propose filter convolution, a novel design that replaces the traditional convolution layer and suppresses coherent speckle noise while extracting features. Specifically, the convolution kernel of the filter convolution comes from the input and is generated by two modules: the kernel-generation module and local weight generation module. The kernel-generation module is a dynamic structure that generates dynamic convolution kernels using input image or feature information. The local weight generation module is based on the statistical characteristics of the input images or features and is used to generate local weights. The introduction of local weights allows the extracted features to contain more local characteristic information, which is conducive to ship detection in SAR images. In addition, we proved that the fusion of the proposed kernel-generation module and the local weight module can suppress coherent speckle noise in the SAR image. The experimental results show the excellent performance of our method on a large-scale SAR ship detection dataset-v1.0 (LS-SSDD-v1.0). It also achieved state-of-the-art performance on a high-resolution SAR image dataset (HRSID), which confirmed its applicability.

Rotated object detection is an extension of object detection that uses an oriented bounding box instead of a general horizontal bounding box to define the object position. It is widely used in remote sensing images, scene text, and license plate recognition. The existing rotated object detection methods usually add an angle prediction channel in the bounding box prediction branch, and smooth $L_1$ loss is used as the regression loss function. However, we argue that smooth $L_1$ loss causes a sudden change in loss and slow convergence due to the angle solving mechanism of open CV (the angle between the horizontal line and the first side of the bounding box in the counter-clockwise direction is defined as the rotation angle), and this problem exists in most existing regression loss functions. To solve the above problems, we propose a decoupling modulation mechanism to overcome the problem of sudden changes in loss. On this basis, we also proposed a constraint mechanism, the purpose of which is to accelerate the convergence of the network and ensure optimization toward the ideal direction. In addition, the proposed decoupling modulation mechanism and constraint mechanism can be integrated into the popular regression loss function individually or together, which further improves the performance of the model and makes the model converge faster. The experimental results show that our method achieves 75.2% performance on the aerial image dataset DOTA (OBB task), and saves more than 30% of computing resources. The method also achieves a state-of-the-art performance in HRSC2016, and saved more than 40% of computing resources, which confirms the applicability of the approach.

Pixel-level classification of very-high-resolution images is a crucial yet challenging task in remote sensing. While transformers have demonstrated effectiveness in capturing dependencies, their tendency to partition images into patches may restrict their applicability to highly detailed remote sensing images. To extract latent contextual semantic information from high-resolution remote sensing images, we proposed a gaze–saccade transformer (GSV-Trans) with visual perceptual attention. GSV-Trans incorporates a visual perceptual attention (VPA) mechanism that dynamically allocates computational resources based on the semantic complexity of the image. The VPA mechanism includes both gaze attention and eye movement attention, enabling the model to focus on the most critical parts of the image and acquire competitive semantic information. Additionally, to capture contextual semantic information across different levels in the image, we designed an inter-layer short-term visual memory module with bidirectional affinity propagation to guide attention allocation. Furthermore, we introduced a dual-branch pseudo-label module (DBPL) that imposes pixel-level and category-level semantic constraints on both gaze and saccade branches. DBPL encourages the model to extract domain-invariant features and align semantic information across different domains in the feature space. Extensive experiments on multiple pixel-level classification benchmarks confirm the effectiveness and superiority of our method over the state of the art.

Domain shifts pose significant challenges for cross-domain semantic segmentation in high-resolution remote sensing imagery. Inspired by the cognitive mechanisms of the human brain, we propose a Brain-Inspired Style Transfer and Semantic Segmentation Collaborative Adversarial Framework (SAF), which mimics neural processes such as hierarchical perception, memory retrieval, and multimodal integration to enhance cross-domain feature alignment and segmentation performance. To achieve the joint optimization of style transfer and semantic segmentation networks, we introduce three key components: a Semantic-Aware Transformer Module (SATM) that dynamically captures and preserves essential semantic features during style transfer; a Semantic-Driven Multi-feature Memory Module (SMM) that stores and retrieves historical style and semantic information to improve domain adaptability; a Domain-Invariant Style-Semantic Center Space (DSCS) that aligns style and semantic features within a shared representation space, mitigating discrepancies between style and semantic domains. Extensive experiments across multiple tasks demonstrate that SAF effectively reduces distortions and semantic inconsistencies by achieving deep style–semantic alignment. Compared to leading approaches, SAF achieves a superior balance between style adaptation and semantic preservation, significantly improving model generalization in remote sensing applications.

Pulsed Eddy Current (PEC) NDT has played a vital role in detection and classification of the surface and sub-surface defects in conductive structures. Normally, it uses peak values of the acquired transient field signals, and the combination of the feature values of the time of the peak to identify flaws with the help of Principal Component Analysis (PCA). However, it is found that the random noise undermines the classification results, because PCA works robustly only in the time domain. In the light of this drawback, the fundamental and the first-harmonic components are investigated and taken as the new feature values in the frequency domain. Through the analysis of the feature values in both time and frequency domains, the influence of random noise is mitigated. Consequently, surface defects, subsurface defects and metal thickness changes are classified with much higher identification accuracy.