In remote sensing scene classification (RSSC), features can be extracted with different spatial frequencies where high-frequency features usually represent detailed information and low-frequency features usually represent global structures. However, it is challenging to extract meaningful semantic information for RSSC tasks by just utilizing high- or low-frequency features. The spatial composition of remote sensing images (RSIs) is more complex than that of natural images, and the scales of objects vary significantly. In this article, a multiscale feature fusion covariance network (MF ² CNet) with octave convolution (Oct Conv) is proposed, which can extract multifrequency and multiscale features from RSIs. First, the multifrequency feature extraction (MFE) module is used to obtain fine-grained frequency features by Oct Conv. Then, the features of different layers in MF ² CNet are fused by the multiscale feature fusion (MF ² ) module. Finally, instead of using global average pooling (GAP), global covariance pooling (GCP) extracts high-order information from RSIs to capture richer statistics of deep features. In the proposed MF ² CNet, the obtained multifrequency and multiscale features can effectively improve the performance of CNNs. Experimental results on four public RSI datasets show that MF ² CNet has advantages in RSSC over current state-of-the-art methods. The source codes of this method can be found at https://github.com/liuqingxin-chd/MF2CNet .

Most synthetic aperture radar (SAR) ship detectors based on convolutional neural networks (CNNs) needed preset anchor boxes to object classification and bounding box coordinate regression. However, the sparsity and unbalanced distribution of ships in SAR images mean that most anchor boxes are redundant. Thus, the anchor settings directly affect the performance and generalization ability of the detector. In addition, a variety in ship scales and the substantial interference of inshore backgrounds bring significant challenges to the SAR ship detector’s performance improvement. In this letter, a novel anchor-free-based detector, named feature balance and united attention (FBUA-Net), is proposed. We adopt a keypoint-based strategy to predict bounding boxes to eliminate the influence of anchors. Besides, we propose a global context-guided feature balanced pyramid (GC-FBP), which balances the semantic information at different levels of the feature pyramid by aggregation and averaging and uses the global context module (GCM) to learn global contextual information to construct long-range dependencies between ship targets and the background. Considering the interference of scattering noise to the detector, a united attention module (UAM) is designed to reduce the interference of surrounding noise by focusing on the spatial shape and scale size of ship targets in both the spatial and scale domains. Experimental results on the SAR ship detection dataset (SSDD) and high-resolution SAR images dataset (HRSID) datasets show that our detector achieves state-of-the-art (SOTA) performance. The source code can be found at https://github.com/so-bright/FBUA-Net .

ELLK-Net, an efficient, lightweight network with a large kernel, is proposed for SAR ship detection. It addresses background variations, different ship scales, and noise interference challenges. ELLK-Net uses an anchor-free detector framework and sequentially decomposes large kernel convolutions to capture comprehensive global information and long-range dependencies. It adaptively selects convolution kernels on the basis of target characteristics, enhancing multiscale feature expression. A novel large kernel multiscale attention module is introduced to enhance interlayer feature fusion and semantic alignment, mitigating the impacts of overlapping ships and scattering noise. Structural reparameterization techniques optimize inference speed across devices without compromising accuracy. The experimental results on the SSDD and HRSID datasets demonstrate that ELLK-Net achieves impressive AP ₅₀ values of 95.6% and 90.6% for horizontal box detection and 89.7% and 79.7% for rotating box detection, respectively. The reparameterized detector exhibits a significant 48.7% FPS improvement on the Nvidia Jetson NX platform, indicating its suitability for edge computing deployment. The code is available at https://github.com/CHD-IPAC/ELLK-Net.

To infer unknown remote sensing scenarios, for remote sensing scene classification (RSSC), most existing deep neural networks (DNNs) are trained on closed datasets. When the acquisition speed and quantity of remote sensing images increase rapidly, these models cannot be used to classify new scenes. Currently, incremental learning as an effective solution for solving the catastrophic forgetting issue, but ignoring the stability-plasticity dilemma. In this article, we propose a new incremental learning network, named efficient channel attention-based multiscale depthwise network (ECA-MSDWNet), in which efficient channel attention (ECA) improves the model’s ability to focus on critical information in complex context, and multiscale depthwise convolution (MSDW Conv) extracts multiscale features in a fine-grained way. In addition, in incremental learning process, we expand new modules based on a dynamic-structure method to fit the residuals between the labels and the outputs of the old model, enhancing the plasticity of the new model for new tasks while maintaining the performance of the old tasks. Finally, we compress the model to reduce redundant parameters and feature dimensions through an effective knowledge distillation strategy. Experiments on four open datasets demonstrate the effectiveness of our method. Our code is available at https://github.com/zhangyu-chd/ECA-MSDWNet .

Remote sensing image (RSI) scene classification plays an active role in many application areas. Due to the excellent performance of the convolutional neural networks (CNNs), which have widely applied in RSI scene classification in recent years. However, most existing methods improve the classification accuracy by improving the model parameters or fusing the features of CNNs. This will make the whole model very complicated and unable to extract multiscale features at a more granular level. This letter proposes a novel and lightweight multiscale depthwise network (MSDWNet) with efficient spatial pyramid attention (ESPA), namely ESPA-MSDWNet, with low model parameters and high accuracy in solving this problem. The ESPA-MSDWNet uses MobileNet V2 as a backbone. We represent multiscale features at a more granular level and expand the receptive fields by multiscale depthwise convolution (MSDW Conv). We also propose the ESPA module to extract dependencies between channels. The ablation experiment verifies the effectiveness of our proposed MSDW Conv and ESPA module. Experimental results on three public RSI datasets show that ESPA-MSDWNet has advantages in classification accuracy and execution efficiency over current state-of-the-art (SOTA) methods.

Identification of insulation defects in gas insulated metal-enclosed switchgear (GIS) is important for partial discharge (PD) evaluation. This article proposes a polar coordinate pattern approach to characterize the different kinds of defect types. These defect types include floating electrodes, a fixed protrusion on the enclosure, surface contamination on the spacer, metallic prominence on the high voltage electrode, a void in the insulator, and free metal particles on the enclosure. First, the physical models for the insulation defects in the established GIS model are designed. Second, the phase resolved pulse sequence (PRPS) data sets are obtained using ultra-high frequency (UHF) measurement. Then, the polar coordinate patterns are proposed to characterize the defects. Nine discharge parameters combined with the parameters based on quadrant statistical theory constitute the input feature vector to identify the PD types. The experimental results show that these new parameters could produce a clear, quantitative description of the characteristics of the defect types and could be used to distinguish between the different kinds of defect types.