Search Results (130)

In this study, we utilize Gaofen-2 satellite remote sensing images to optimize and enhance the extraction of feature information from rural compounds, addressing key challenges in high-resolution remote sensing analysis: traditional methods struggle to effectively capture long-distance spatial dependencies for scattered rural compounds. To this end, we implement the RS³Mamba+ deep learning model, which introduces the Mamba state space model (SSM) into its auxiliary branching—leveraging Mamba’s sequence modeling advantage to efficiently capture long-range spatial correlations of rural compounds, a critical capability for analyzing sparse rural buildings. This Mamba-assisted branch, combined with multi-directional selective scanning (SS2D) and the enhanced STEM network framework (replacing single 7 × 7 convolution with two-stage 3 × 3 convolutions to reduce information loss), works synergistically with a ResNet-based main branch for local feature extraction. We further introduce a multiscale attention feature fusion mechanism that optimizes feature extraction and fusion, enhances edge contour extraction accuracy in courtyards, and improves the recognition and differentiation of courtyards from regions with complex textures. The feature information of courtyard utilization status is finally extracted using empirical methods. A typical rural area in Weifang City, Shandong Province, is selected as the experimental sample area. Results show that the extraction accuracy reaches an average intersection over union (mIoU) of 79.64% and a Kappa coefficient of 0.7889, improving the F1 score by at least 8.12% and mIoU by 4.83% compared with models such as DeepLabv3+ and Transformer. The algorithm’s efficacy in mitigating false alarms triggered by shadows and intricate textures is particularly salient, underscoring its potential as a potent instrument for the extraction of rural vacancy rates. Full article

Most biomarkers exhibit abnormal expression in more than one disease, making conventional single-biomarker detection strategies prone to false-negative results. Detecting multiple biomarkers associated with a single disease can therefore substantially improve diagnostic accuracy. Accordingly, recent research has focused on precise multiplex detection, leading to the development of sensors employing various readout methods, including electrochemical, fluorescence, Raman, and colorimetric approaches. This review focuses on optical sensing applications, such as fluorescence, Raman spectroscopy, and colorimetry, which offer rapid and straightforward detection and are well suited for point-of-care testing (POCT). These optical sensors exploit nanoscale phenomena derived from the intrinsic properties of nanomaterials, including metal-enhanced fluorescence (MEF), Förster resonance energy transfer (FRET), and surface-enhanced Raman scattering (SERS), which can be tailored through modifications in material type and structure. We summarize the types and properties of commonly used nanomaterials, including plasmonic and carbon-based nanoparticles, and provide a comprehensive overview of recent advances in multiplex biomarker detection. Furthermore, we address the potential of these nanosensors for clinical translation and POCT applications, highlighting their relevance for next-generation disease diagnostic platforms. Full article

Accurate quantification of minor mineral phases is important in Powder X-Ray Diffraction (PXRD) and Rietveld phase quantification. The precise limit of quantification for the various phases is rarely considered but rather approximated to 0.2–2 wt% by applying a global minimum weight percentage threshold. This approximation often leads to false positive or false negative phase quantity, jeopardizing the trustworthiness of the analytic method in general. In this work (1) we propose a dynamic and adaptable false positive filtering method for Rietveld Quantitative X-ray diffraction (QXRD) based on a phase-specific signal-to-noise ratio referred to as “Phase-SNR”; (2) we introduce the method baptized “Phase Guard” which is implemented in the software HighScore Plus. Phase Guard is based on peaks counting statistics and it automatically adapts to different mineral scattering powers, different mineral crystallinity, instrumental configuration and measurement time. Its applicability and benefits are demonstrated with several examples in cement and mining applications. The adoption of Phase Guard is especially beneficial for industrial black-box solutions, where all “probable” phases are included in the model, even when they are absent from the sample. Phase Guard eliminates false positives, it reduces the likelihood of false negatives, and it is an essential tool to answer the question “what is the limit of quantification for Rietveld analysis?” Full article

Progress in clinical diagnosis increasingly relies on innovative technologies and advanced disease biomarker detection methods. While cell labeling remains a well-established technique, label-free approaches offer significant advantages, including reduced workload, minimal sample damage, cost-effectiveness, and simplified chip integration. These approaches focus on the morpho-biophysical properties of cells, eliminating the need for labeling and thus reducing false results while enhancing data reliability and reproducibility. Current label-free methods span conventional and advanced technologies, including phase-contrast microscopy, holographic microscopy, varied cytometries, microfluidics, dynamic light scattering, atomic force microscopy, and electrical impedance spectroscopy. Their integration with artificial intelligence further enhances their utility, enabling rapid, non-invasive cell identification, dynamic cellular interaction monitoring, and electro-mechanical and morphological cue analysis, making them particularly valuable for cancer diagnostics, monitoring, and prognosis. This review compiles recent label-free cancer cell detection developments within clinical and biotechnological laboratory contexts, emphasizing biophysical alterations pertinent to liquid biopsy applications. It highlights interdisciplinary innovations that allow the characterization and potential identification of cancer cells without labeling. Furthermore, a comparative analysis addresses throughput, resolution, and detection capabilities, thereby guiding their effective deployment in biomedical research and clinical oncology settings. Full article

To address the vulnerability of single-polarization deception jamming and simply modulated dual-polarization jamming to discrimination by dual-polarization radars, this paper proposes a deception jamming method based on joint fast–slow-time polarization modulation (FSPMJ). First, in the slow-time domain (across multiple pulses), the polarization azimuth of the jamming signal is designed according to the target’s polarization ratio distribution. Subsequently, with the target polarization degree as the optimization objective, the polarization phase difference of the jamming signal is solved using an interior-point optimization algorithm, establishing the initial polarization state for each pulse. This process is iterated to design the polarization state for the first half of each pulse. Then, in the fast-time domain (within a single pulse), a polarization state orthogonal to the pre-generated first-half state, is constructed to serve as the polarization state for the latter half of each pulse. Finally, the effectiveness of the proposed method is validated through combined simulation and measured data using a Support Vector Machine (SVM) algorithm. Results demonstrate that compared to single-polarization deception jamming and existing polarization-modulated jamming, this method reduces the false target discrimination rate of dual-polarization radars by 35.4% without requiring complex target scattering matrices. Full article

Due to the sparse distribution of ship targets in wide-area offshore scenarios, the typical cascade mode of imaging and detection for space-borne Synthetic Aperture Radar (SAR) echo data would consume substantial computational time and resources, severely affecting the timeliness of ship target information acquisition tasks. Therefore, we propose a ship target integrated imaging and detection framework (ST-IIDF) for SAR oceanic region data. A two-step filtering structure is added in the SAR imaging process to extract the potential areas of ship targets, which can accelerate the whole process. First, an improved peak-valley detection method based on one-dimensional scattering characteristics is used to locate the range gate units for ship targets. Second, a dynamic quantization method is applied to the imaged range gate units to further determine the azimuth region. Finally, a lightweight YOLO neural network is used to eliminate false alarm areas and obtain accurate positions of the ship targets. Through experiments on Hisea-1 and Pujiang-2 data, within sparse target scenes, the framework maintains over 90% accuracy in ship target detection, with an average processing speed increase of 35.95 times. The framework can be applied to ship target detection tasks with high timeliness requirements and provides an effective solution for real-time onboard processing. Full article

The Tomographic Synthetic Aperture Radar (TomoSAR) technique is widely used for monitoring urban infrastructures, as it enables the mapping of individual scatterers across additional dimensions such as height (3D), thermal dilation (4D), and deformation velocity (5D). Retrieving this information is crucial for building management and maintenance. Nevertheless, accurately extracting it from TomoSAR data poses several challenges, particularly the presence of outliers due to uneven and limited baseline distributions. One way to address these issues is through statistical detection approaches such as the Generalized Likelihood Ratio Test, which ensures a Constant False Alarm Rate. While effective, these methods face two primary limitations: high computational complexity and the off-grid problem caused by the discretization of the search space. To overcome these drawbacks, we propose an approach that combines a quick initialization process using Fast-Sup GLRT with local descent optimization. This method operates directly in the continuous domain, bypassing the limitations of grid-based search while significantly reducing computational costs. Experiments conducted on both simulated and real datasets acquired with the TerraSAR-X satellite over the Spanish city of Barcelona demonstrate the ability of the proposed approach to maintain computational efficiency while improving scatterer localization accuracy in the third and fourth dimensions. Full article

Real-time Synthetic Aperture Radar (SAR) imaging for small Unmanned Aerial Vehicles (UAVs) has become a significant research focus. However, limitations in Size, Weight, and Power (SwaP) restrict the imaging quality and timeliness of small UAV-borne SAR, limiting its practical application. This paper presents a non-iterative real-time Feature Sub-image Based Stripmap Phase Gradient Autofocus (FSI-SPGA) algorithm. The FSI-SPGA algorithm combines 2D Constant False Alarm Rate (CFAR) for coarse point selection and spatial decorrelation for refined point selection. This approach enables the accurate extraction of high-quality scattering points. Using these points, the algorithm constructs a feature sub-image containing comprehensive phase error information and performs a non-iterative phase error estimation based on this sub-image. To address the multifunctional, low-power, and real-time requirements of small UAV SAR, we designed a highly efficient hybrid architecture. This architecture integrates dataflow reconfigurability and dynamic partial reconfiguration and is based on an ARM + FPGA platform. It is specifically tailored to the computational characteristics of the FSI-SPGA algorithm. The proposed scheme was assessed using data from a 6 kg small SAR system equipped with centimeter-level INS/GPS. For SAR images of size 4096 × 12,288, the FSI-SPGA algorithm demonstrated a 6 times improvement in processing efficiency compared to traditional methods while maintaining the same level of precision. The high-efficiency reconfigurable ARM + FPGA architecture processed the algorithm in 6.02 s, achieving 12 times the processing speed and three times the energy efficiency of a single low-power ARM platform. These results confirm the effectiveness of the proposed solution for enabling high-quality real-time SAR imaging under stringent SwaP constraints. Full article

Our work presents a laser fuze detector structure with a four-channel center-symmetrical emitting laser under the influence of the three-dimensional (3D) and spatial properties of smoke clouds, which was used to improve the laser fuze’s anti-smoke interference ability, as well as the target detection performance. A laser echo signal model under multiple frequency-modulated continuous-wave (FMCW) lasers was constructed by investigating the hybrid collision scattering process of photons and smoke particles. Using a virtual particle system implemented in Unity3D, the laser target characteristics were studied under the conditions of multiple smoke particle characteristic variations. The simulation results showed that false alarms in low-visibility and missed alarms in high-visibility smoke scenes could be effectively solved with four emitting lasers. With this structure of the laser fuze prototype, the smoke echo signal and the target echo signal could be separated, and the average amplitude growth rate of the target echo signal was improved. The conclusions are supported by the results of experiments. Therefore, this study not only reveals laser target properties for 3D and spatial properties of particles, but also provides design guidance and reasonable optimization of FMCW laser fuze multi-channel emission structures in combination with multi-particle collision types and target characteristics. Full article

Due to the complex structure of high-rise space buildings, traditional point fire detectors are not effective in terms of detection range and installation difficulty. Although linear beam smoke detectors are widely adopted, they still face problems such as low accuracy and false alarms caused by interference. To address these limitations, we constructed a 120 m experimental platform for analyzing smoke–light interactions. Through systematic investigation of spectral scattering phenomena, optimal operational wavelengths were identified for beam-type detection. By improving the gated recurrent unit (GRU) neural network, an algorithm combining dual-wavelength information fusion and an attention mechanism was designed. The algorithm integrates dual-wavelength information and introduces the cross-attention mechanism into the GRU network to achieve collaborative modeling of microscale scattering characteristics and macroscale concentration changes of smoke particles. The alarm strategy based on time series accumulation effectively reduces false alarms caused by instantaneous interference. The experiment shows that our method is significantly better than traditional algorithms in terms of accuracy (96.8%), false positive rate (2.1%), and response time (6.7 s). Full article

This study aims to elucidate the optimal exploitation of polarimetric scattering information in dual-pol SAR data. For an effective comparison of the flood detection performance between dual-pol parameters, we presented a simple fuzzy-based flood detection algorithm. Scattering characteristics of water surface and non-water land can vary depending on the region and flood conditions. Therefore, the flood detection performance of the dual-pol parameters was evaluated across three datasets with different geographic, climatic, and land cover conditions. The results demonstrated that accurate and stable performance in the detection of inundated areas under different surface conditions can be achieved by combining water body information from dual-pol channels in a disjunctive way. It also suggests that synergy in flood detection can be expected when using polarization observation data by considering each polarimetric channel as an independent information source and combining them rather than deriving the most relevant polarization parameter. Furthermore, combining common information from two dual-pol channels in a conjunctive way could provide the most reliable SAR flood detection results with minimum false alarms from the user’s perspective. Based on these experimental results, a two-class flood classification scheme was proposed for improving the applicability of SAR remote sensing in identifying flooded areas. Full article

Due to issues such as low resolution, scattering noise, and background clutter, ship detection in Synthetic Aperture Radar (SAR) images remains challenging, especially in inshore regions, where these factors have similar scattering characteristics. To overcome these challenges, this paper proposes a novel SAR ship detection framework that integrates adaptive channel attention with large kernel adaptation. The proposed method improves multi-scale contextual information extraction by enhancing feature map interactions at different scales. This method effectively reduces false positives, missed detections, and localization ambiguities, especially in complex inshore environments. Also, it includes an adaptive channel attention block that adjusts attention weights according to the dimensions of the input feature maps, enabling the model to prioritize local information and improve sensitivity to small object features in SAR images. In addition, a large kernel attention block with adaptive kernel size is introduced to automatically adjust the receptive field designed to extract abundant context information at different detection layers. Experimental evaluations on the SSDD and Hysid SAR ship datasets indicate that our method achieves excellent detection performance compared to current methods, as well as demonstrate its effectiveness in overcoming SAR ship detection challenges. Full article

Circular synthetic aperture radar (CSAR) is an advanced imaging mechanism with three-dimensional (3D) imaging capability, enabling the acquisition of omnidirectional scattering information for observation regions. The existing 3D point cloud reconstruction method for single-pass CSAR is capable of obtaining the 3D scattering points for targets by inversely mapping the projection points in multi-aspect sub-aperture images and subsequently voting on the scattering candidates. However, due to the influence of non-target background points in multi-aspect sub-aperture images, there are several false points in the 3D reconstruction result, which affect the quality of the produced 3D point cloud. In this paper, we propose a novel 3D point cloud reconstruction method for single-pass CSAR based on inverse mapping with target contour constraints. The proposed method can constrain the range and height of inverse mapping by extracting the contour information of targets from multi-aspect sub-aperture CSAR images, which contributes to improving the reconstruction quality of 3D point clouds for targets. The performance of the proposed method was verified based on X-band CSAR measured data sets. Full article

Stepped-frequency radar is an important high range resolution radar. It can achieve wide overall bandwidth with narrow instant bandwidth. When the signal-to-noise ratio is low, detection and tracking become challenging due to dense false alarms and the range-Doppler coupling problem. In this paper, a new methodology is presented to address this problem. A Bernoulli extended target filter is used for joint detection and tracking of a dim range-spread target. The results of coherent processing are thresholded firstly using a low threshold, and the range-Doppler coupled detections generated by multiple scatterers, together with the false alarms, are fed to the Bernoulli filter. By appropriately modeling the range-Doppler coupling, the range spread, and the false alarms in the state and measurement models, the filter can detect the target effectively. Simulation results show that good detection performance is obtained and the range-Doppler coupling is decoupled simultaneously. Full article

Polarimetric decomposition methods are widely used in polarimetric Synthetic Aperture Radar (SAR) data processing for extracting scattering characteristics of targets. However, polarization SAR methods for ship detection still face challenges. The traditional constant false alarm rate (CFAR) detectors face sea clutter modeling and parameter estimation problems in ship detection, which is difficult to adapt to the complex background. In addition, neural network-based detection methods mostly rely on single polarimetric-channel scattering information and fail to fully explore the polarization properties and physical scattering laws of ships. To address these issues, this study constructed two novel characteristics: a helix-scattering enhanced (HSE) local component and a multi-scattering intensity difference (MSID) edge component, which are specifically designed to describe ship scattering characteristics. Based on the characteristic differences of different scattering components in ships, this paper designs a context aggregation network enhanced by local and edge component characteristics to fully utilize the scattering information of polarized SAR data. With the powerful feature extraction capability of a convolutional neural network, the proposed method can significantly enhance the distinction between ships and the sea. Further analysis shows that HSE is able to capture structural information about the target, MSID can increase ship–sea separation capability, and an HV channel retains more detailed information. Compared with other decomposition models, the proposed characteristic combination model performs well in complex backgrounds and can distinguish ship from sea more effectively. The experimental results show that the proposed method achieves a detection precision of 93.6% and a recall rate of 91.5% on a fully polarized SAR dataset, which are better than other popular network algorithms, verifying the reasonableness and superiority of the method. Full article