With the continuous evolution of radar RF sensing technology in the Internet of Things (IoT) field, the deployment of co-frequency communication devices within smart home and healthcare environments is becoming increasingly prevalent. Concurrently, the proliferation of RF signals has rendered the challenge of spectrum resource allocation increasingly prominent, exacerbating the issue of severe mutual interference among these co-frequency devices. This severe interference exhibits intricate attributes, characterized by prolonged duration, a broad frequency range, and high level power intensity. When experiencing interference, the target’s echo is notably veiled, rendering recovery through standard technologies exceedingly challenging. From our perspective, in the presence of severe interference, the emphasis ought to be on elegantly reconstructing the target’s echo located in the period of interference, rather than merely eliminating the negative impact on interferences from the raw radar signals. Based on this thought, we have explored the feasibility of a deep network model for interference mitigation of radar signals in this paper, and introduced an interference mitigation method, namely Ra-SPD. The Ra-SPD is crafted through a dual design methodology, integrating the mask-guided spectral decomposition mechanism alongside the region-aware spatial decomposition scheme. The primary objective of the initial design is to enhance the capabilities of the standard deep model, enabling it to distinguish the duration of interference present in radar signals, particularly when the semantic alignment between the restored radar signal and the corresponding ground truth is at risk of being disrupted. Meanwhile, the improvement of local information related to the restored radar signal is accomplished using the spatial decomposition scheme, aimed at increasing the robustness of low-intensity signal segments against severe interference. Experimental results demonstrate superior performance of Ra-SPD at 15%, 40%, and 80% three different signal-to-interference ratio conditions, achieving PSNR: 35.73/32.99/30.28 dB; SSIM: 0.96/0.95/0.92; FID: 23.41/30.97/43.92; and MAE: 0.014/0.024/0.036 across conditions. Our method consistently outperforms six benchmark algorithms in all objective metrics and subjective evaluations, exhibiting 7.6%, 1.5%, 5.5%, and 17.2% average improvements in four metrics relative to the suboptimal method, highlighting significant advantages in interference mitigation and signal quality preservation.

The fundamental goal of hyperspectral anomaly detection (HAD) is the identification of pixels manifesting substantial deviations in spectral attributes when compared to their neighboring pixels. Nevertheless, the intrinsic attributes of hyperspectral images (HSI), characterized by their high-dimensional essence and the interdependencies among spectral bands, frequently exert an influence on the efficacy of anomaly detection( AD). Furthermore, current detection algorithms often fall short in harnessing the inherent information encapsulated within HSI, thereby constraining the network’s expressive potential. In response to these challenges, we introduce a multiview model tailored that amalgamates both global and local features for HAD. Specifically, the proposed method employs an unsupervised learning-based multiview network to simultaneously conduct feature analysis on both global and local attributes within HSI. The model incorporates a dual-component structure, featuring a global module utilizing axial attention for comprehensive global attribute analysis, and a local module employing a convolutional neural network with residual connections to capture fine-grained local features. Subsequently, the global-local multiview anomaly coupling mechanism is applied to consolidate the strengths of distinct perspectives, resulting in the ultimate AD outcomes. Experimental evaluations are performed on seven public HSI datasets, demonstrating superior performance of the proposed method in comparison to other state-of-the-art approaches.

Hyperspectral anomaly detection (HAD) is of great value in both practical and theoretical terms. However, due to the lack of available semantic labels, previous works mainly relied on unsupervised or semi-supervised methods to construct learning models, which inevitably lacked semantic guidance and led to limited anomaly detection (AD) effectiveness. Besides, few previous methods jointly mine spectral and spatial global dependencies, which limits their effectiveness in practical scenarios. To address the above problems, we design a novel self-supervised HAD method, named the Self-Supervised Hyperspectral Anomaly Detection method based on the Finite Spatial-wise Attention. The core of proposed method is the designed Self-Supervised Hyperspectral Anomaly Detection transFormer (SSHADFormer). It explores the specific spectral attributes of hyperspectral images (HSIs) to reconstruct background HSI from a given RGB image, which solves the difficulty of acquiring semantic information and enhances the agility of AD models. In addition, we propose a Finite Spatial-wise Attention mechanism. The mechanism mines the cluster structure of the background spectrum in a data-driven manner, enhancing the discriminative ability between background and anomalous targets while avoiding anomalous targets interference during training. Extensive experiments on six public datasets demonstrate the effectiveness and agility of the proposed method.

Small object detection in large-scale remote sensing images (RSIs) is crucial for military and civil applications, but it remains challenging. Since small objects occupy few pixels, their features are easily interfered with by complex backgrounds and large objects. In addition, they are susceptible to localization offsets, which are prone to false or missed detections as there are few predicted bounding boxes matching the ground truth. To overcome these issues, this article proposes a filter progressive small object detection (FPSOD) model that is based on the progressive mechanism. With the proposed attention-based soft-threshold filtering module, FPSOD significantly filters out redundant information in high-level feature maps thus enhancing the semantic features of small objects. Furthermore, a progressive regression loss (PR-Loss) function is proposed to facilitate the precise localization, which mitigates predicted bounding box drift by limiting the fluctuated range of the gradients. The experimental results show that the proposed model substantially improves the precision and recall of small objects, effectively reduces missed detections, and improves detection performance.

Gesture and sign language (SL) recognition technology enables machines to understand the meaning of human hand movements. In human–computer interaction, it is expected that gesture/SL recognition technologies will overcome equipment size and application environment constraints; in information communication, gesture/SL recognition technology will assist healthy people in more easily entering the world of deaf people and better understanding and meeting their inner emotional needs; and in patient monitoring, gesture/SL recognition technologies will detect abnormal behavior in the elderly or patients and reduce possible safety issues. As a result, it has significant implications for both research and broad application. Because the radar sensor can work normally in a wide range of illumination and weather conditions, as well as penetrate the shelter to receive the moving object echo signal and preserve individual privacy, it is becoming increasingly popular in a variety of recognition tasks. A primary step in using radar sensors for gesture/SL recognition is to suppress clutter to highlight useful motion information. To evaluate the clutter suppression effect, however, an objective metric is required. We present an objective assessment metric based on the pseudo-reference image (PRI) and an automatic threshold selection method based on Otsu for clutter suppression, as well as subjective and objective experiments demonstrating their effectiveness and universality in gesture/SL recognition. Notably, our proposed metric can be used for any recognition task that uses micro-Doppler (MD) spectrograms as the dataset.

Hyperspectral anomaly detection (HAD) aims to identify anomalous pixels in the image with significant spectral differences from their surrounding background pixels, and has important military and civilian applications. However, challenges such as high spectral similarity between pixels, data redundancy, and lack of prior information pose significant difficulties in HAD. To address these issues, we propose the Selective Siamese Graph Guided Hyperspectral Anomaly Detection method. In the initial phase of this study, we present a spatial-spectral feature dynamic composition module. Guided by the principles of three-way clustering theories, this module excels in achieving heightened precision in feature embedding and graph construction. Subsequently, we introduce the Characteristic Expression Differentiation mechanism, designed to enhance the separation of hidden layer features for heterogeneous data in high-dimensional space by incorporating Siamese network-derived similarity discrimination principles. Lastly, we develop a graph-guided Siamese selective reconstruction module that places a strong emphasis on the differentiation between background and anomaly features. It utilizes background graph data to construct a pure background and concurrently establishes connections across spectral bands. This approach significantly enhances data processing efficiency while reducing computational resource consumption through the elimination of redundancy. Extensive experiments on seven public datasets demonstrate the effectiveness of the method.

Micro-Doppler-based human activity recognition has been extensively researched in remote sensing. However, a well-performing classifier requires sufficient radar data of omnidirectional human movements due to the “angle sensitivity”, resulting in high costs for radar data acquisition. To address this issue, this study defines for the first time the task of “omnidirectional spectrogram generation” and proposes a method to obtain enough omnidirectional spectrograms based on the spectrograms of human movements in one direction. It significantly reduces the dependence on radar measurements with omnidirectional setups. Our method is founded upon an image translation framework that is enhanced by incorporating the concept of information disentanglement and a proposed feature-level unbiased domain translation strategy. They enable us to generate high-quality omnidirectional spectrograms at various aspect angles. The generated spectrograms are then used as training support of omnidirectional micro-Doppler-based classifiers. Subsequently, we conduct an in-depth analysis of the metric correlation between the quality of generated spectrograms and the performance of these classifiers. Finally, we introduce a method for evaluating this correlation by proposed criterion. Our method is evaluated based on a radar simulation dataset, and the results show that it significantly exceeds the compared methods, demonstrating its great potential for the task of omnidirectional recognition of human activities. Besides, we find that there is a significant correlation between classification accuracy and several image quality assessment metrics, and we believe that this investigation will serve as the foundation for future research on assessing the contribution of data generation methods to downstream tasks using quantitative measures.

Perceptual image quality assessment (IQA) plays an important role in numerous applications, including image restoration, compression, enhancement, and others. Although many works have been conducted on individually distorted IQA problems and have achieved encouraging results, few studies have been conducted on multiple distorted (MD) IQA problems. Thus, limited progress has been made. In this paper, we propose a novel no reference image quality assessment (NR-IQA) method, named improved multiscale local binary pattern (IMLBP), for addressing multiply distorted IQA problems. The image structures are sensitive to image distortions, which motivates us to utilize the structural characteristics for overall image quality prediction. We improved the local binary pattern (LBP) by considering the human visual mechanism to better extract the structural information. The IMLBP contains two parts, the LBP and the radius difference LBP (DLBP). The DLBP reflects the values' changes in the radial direction. Specifically, when the radius value is small, the proposed descriptor is computed to represent microstructural information. Conversely, it represents macrostructural information when the radius becomes large. Moreover, to better mimick the human visual mechanism, the IMLBP is computed with the multiscale strategy and the operation is based on a patch unit whose size is proportional to the radius value. The frequency histogram of feature maps is transformed to feature vectors. Subsequently, a predictable function trained by the support vector regression is used to infer the overall quality score. Experimental results show that the proposed method outperforms most state-of-the-art IQA metrics on publicly available multiply distorted image databases.