Search Results (17,451)

The accurate identification of rice growth stages is critical for precision agriculture, crop management, and yield estimation. Remote sensing technologies, particularly multimodal approaches that integrate high spatial and hyperspectral resolution imagery, have demonstrated great potential in large-scale crop monitoring. Multimodal data fusion offers complementary and enriched spectral–spatial information, providing novel pathways for crop growth stage recognition in complex agricultural scenarios. However, the lack of publicly available multimodal datasets specifically designed for rice growth stage identification remains a significant bottleneck that limits the development and evaluation of relevant methods. To address this gap, we present RiceStageSeg, a multimodal benchmark dataset captured by unmanned aerial vehicles (UAVs), designed to support the development and assessment of segmentation models for rice growth monitoring. RiceStageSeg contains paired centimeter-level RGB and 10-band multispectral (MS) images acquired during several critical rice growth stages, including jointing and heading. Each image is accompanied by fine-grained, pixel-level annotations that distinguish between the different growth stages. We establish baseline experiments using several state-of-the-art semantic segmentation models under both unimodal (RGB-only, MS-only) and multimodal (RGB + MS fusion) settings. The experimental results demonstrate that multimodal feature-level fusion outperforms unimodal approaches in segmentation accuracy. RiceStageSeg offers a standardized benchmark to advance future research in multimodal semantic segmentation for agricultural remote sensing. The dataset will be made publicly available on GitHub v0.11.0 (accessed on 1 August 2025). Full article

Background/Objectives: Spinal diffusion tensor imaging (sDTI) remains a challenging method for the selective evaluation of key anatomical structures, like pyramidal tracts (PTs) and dorsal columns (DCs), and for reliably quantifying diffusion metrics such as fractional anisotropy (FA), radial diffusivity (RD), mean diffusivity (MD), and axial diffusivity (AD). This prospective, single-center study aimed to assess the reproducibility, robustness, and reliability of an optimized axial sDTI protocol, specifically intended for long fiber tracts. Methods: We developed an optimized Stejskal–Tanner sequence for high-resolution, axial sDTI of the cervical spinal cord at 3.0 T. Using advanced standardized evaluation and post-processing methods, we estimated DTI values for PTs, DCs, and AHs at the level of the second cervical vertebra. Reliability was evaluated through repeated measurements in 16 healthy volunteers and by comparing results from two 3.0 T scanners (Magnetom Skyra and Magnetom Prisma, Siemens Healthineers, Erlangen, Germany). Reproducibility was assessed using paired t-tests, intraclass correlation coefficients (ICCs), Bland–Altman analysis, and coefficients of variation (CVs). Results: The optimized sDTI protocol demonstrated high consistency for FA between test–retest sessions and across scanners. For the Skyra, the DC region showed the highest reliability (average ICC = 0.858) followed by the PT region (average ICC = 0.789). On the Prisma, the PT region reached an average ICC of 0.854, with the DC region at 0.758. Pooled inter-scanner data indicated good-to-excellent agreement, particularly in the PT region (average ICC = 0.860). FA CVs remained low (<10%) across all regions and scanners. RD showed good-to-excellent ICC values for PTs and DCs (average ICC for Skyra 0.642 and 0.769 and 0.926 and 0.830 for Prisma, respectively) but showed a higher CV between 14.6 and 19.4% for these two scanners. Conclusions: Improved sDTI offers highly reproducible FA measurements for all metrics with scanner independence, supporting its potential as a robust tool for detecting and monitoring spinal cord pathologies. Full article

The accurate classification of staple crops is of great significance for scientifically promoting food production. Crop classification methods based on deep learning models or medium/low-resolution images have been applied in plain areas. However, existing methods perform poorly in complex mountainous scenes with rugged terrain, diverse planting structures, and fragmented farmland. This study introduces the Complex Scene Crop Classification U-Net+ (CSCCU+), designed to improve staple crop classification accuracy in intricate landscapes by integrating supplementary spectral information through an additional branch input. CSCCU+ employs a multi-branch architecture comprising three distinct pathways: the primary branch, auxiliary branch, and supplementary branch. The model utilizes a multi-level feature fusion architecture, including layered integration via the Shallow Feature Fusion (SFF) and Deep Feature Fusion (DFF) modules, alongside a balance parameter for adaptive feature importance calibration. This design optimizes feature learning and enhances model performance. Experimental validation using GaoFen-2 (GF-2) imagery in Xifeng County, Guizhou Province, China, involved a dataset of 2000 image patches (256 × 256 pixels) spanning seven categories. The method achieved corn and rice classification accuracies of 89.16% and 88.32%, respectively, with a mean intersection over union (mIoU) of 87.04%, outperforming comparative models (U-Net, DeeplabV3+, and CSCCU). This research paves the way for staple crop classification in complex land surfaces using high-resolution imagery, enabling accurate crop mapping and providing robust data support for smart agricultural applications. Full article

The aim of this study was to address the issue of significant performance degradation in existing defogging algorithms under extreme fog conditions. Traditional Taylor series-based deformable convolutions are limited by local approximation errors, while the heavy-tailed characteristics of the Cauchy distribution can more successfully model outliers in fog images. The following improvements are made: (1) A displacement generator based on the inverse cumulative distribution function (ICDF) of the Cauchy distribution is designed to transform uniform noise into sampling points with a long-tailed distribution. A novel double-peak Cauchy ICDF is proposed to dynamically balance the heavy-tailed characteristics of the Cauchy ICDF, enhancing the modeling capability for sudden changes in fog concentration. (2) An innovative Cauchy–Gaussian fusion module is proposed to dynamically learn and generate hybrid coefficients, combining the complementary advantages of the two distributions to dynamically balance the representation of smooth regions and edge details. (3) Tree-based multi-path and cross-resolution feature aggregation is introduced, achieving local–global feature adaptive fusion through adjustable window sizes (3/5/7/11) for parallel paths. Experiments on the RESIDE dataset demonstrate that the proposed method achieves a 2.26 dB improvement in the peak signal-to-noise ratio compared to that obtained with the TaylorV2 expansion attention mechanism, with an improvement of 0.88 dB in heavily hazy regions (fog concentration > 0.8). Ablation studies validate the effectiveness of Cauchy distribution convolution in handling dense fog and conventional lighting conditions. This study provides a new theoretical perspective for modeling in computer vision tasks, introducing a novel attention mechanism and multi-path encoding approach. Full article

With the advancement of precision agriculture, Unmanned Aerial Vehicle (UAV)-based remote sensing has been increasingly employed for monitoring crop water and nutrient status due to its high flexibility, fine spatial resolution, and rapid data acquisition capabilities. This review systematically examines recent research progress and key technological pathways in UAV-based remote sensing for crop water and nutrient monitoring. It provides an in-depth analysis of UAV platforms, sensor configurations, and their suitability across diverse agricultural applications. The review also highlights critical data processing steps—including radiometric correction, image stitching, segmentation, and data fusion—and compares three major modeling approaches for parameter inversion: vegetation index-based, data-driven, and physically based methods. Representative application cases across various crops and spatiotemporal scales are summarized. Furthermore, the review explores factors affecting monitoring performance, such as crop growth stages, spatial resolution, illumination and meteorological conditions, and model generalization. Despite significant advancements, current limitations include insufficient sensor versatility, labor-intensive data processing chains, and limited model scalability. Finally, the review outlines future directions, including the integration of edge intelligence, hybrid physical–data modeling, and multi-source, three-dimensional collaborative sensing. This work aims to provide theoretical insights and technical support for advancing UAV-based remote sensing in precision agriculture. Full article

Background: Keratinocyte carcinomas (KCs), including basal cell carcinomas (BCCs) and squamous cell carcinomas (SCCs), are the most prevalent malignancies globally, particularly affecting sun-exposed facial areas. Achieving clear surgical margins in these regions is essential to ensure oncologic control while preserving cosmetic outcomes. Reflectance confocal microscopy (RCM) is a noninvasive imaging technique that enables real-time, high-resolution visualization of skin structures and may aid in margin assessment during KC surgery. This systematic review aims to evaluate the role of in vivo RCM in the surgical treatment of KCs. Methods: This review followed PRISMA guidelines. A comprehensive search of PubMed, Scopus, Web of Science, Medline, and EBSCO databases was conducted for studies published between January 1992 and December 2024. Inclusion criteria focused on clinical studies utilizing in vivo RCM for diagnostic or surgical applications in KC management. Results: Eighteen studies involving 1112 patients were included. RCM was used preoperatively in 5 studies and intraoperatively in another 5. Nine studies assessed margin delineation, while eight focused on diagnostic accuracy. RCM improved diagnostic confidence and allowed for more precise margin assessment, potentially reducing the extent of surgical excision in cosmetically sensitive areas. However, its broader clinical adoption is limited by operator dependency, procedural complexity, and lack of standardization. Conclusions: RCM shows promise as a supportive tool in KC surgery, particularly for preoperative planning. While its diagnostic utility is well established, its intraoperative role requires further validation. Larger, standardized, and cost-effective studies are needed to confirm its impact on surgical outcomes and patient quality of life. Full article

This paper addresses the problem of subsurface target localization from single-snapshot multimonostatic and multifrequency radar measurements. In this context, the use of subspace projection methods—known for their super-resolution capabilities—is hindered by the rank deficiency of the data correlation matrix and the lack of a Vandermonde structure, especially in near-field configurations and layered media. To overcome this issue, we propose a novel pre-processing strategy that transforms the measured data into the $\omega -k$ domain, thereby restoring the structural conditions required for subspace-based detection. The resulting algorithm, referred to as $\omega -k$ MUSIC, enables the application of subspace projection techniques in scenarios where traditional smoothing procedures are not viable. Numerical experiments in a 2-D scalar configuration demonstrate the effectiveness of the proposed method in terms of resolution and robustness under various noise conditions. A Monte Carlo simulation study is also included to provide a quantitative assessment of localization accuracy. Comparisons with conventional migration imaging highlight the superior performance of the proposed approach. Full article

This paper presents a deep learning-based system designed for generating, storing, and retrieving embeddings, specifically tailored for analyzing craquelure networks in paintings. Craquelure, the fine pattern of the craquelure network formed on a painting’s surface over time, is a unique “fingerprint” for artwork item authentication. The system utilizes a modified VGG19 backbone, which effectively balances computational efficiency with the ability to extract rich, multi-scale features from high-resolution grayscale images. By leveraging this architecture, the model captures global structural patterns and local texture information, which are essential for reliable analysis. Full article

Advancements in satellite sensor technology have enabled access to diverse remote sensing (RS) data from multiple platforms. Hyperspectral Image (HSI) data offers rich spectral detail for material identification, while LiDAR captures high-resolution 3D structural information, making the two modalities naturally complementary. By fusing HSI and LiDAR, we can mitigate the limitations of each and improve tasks like land cover classification, vegetation analysis, and terrain mapping through more robust spectral–spatial feature representation. However, traditional multi-scale feature fusion models often struggle with aligning features effectively, which can lead to redundant outputs and diminished spatial clarity. To address these issues, we propose the Cross Attention Bridge for HSI and LiDAR (CAB-HL), a novel dual-path framework that employs a multi-stage cross-attention mechanism to guide the interaction between spectral and spatial features. In CAB-HL, features from each modality are refined across three progressive stages using cross-attention modules, which enhance contextual alignment while preserving the distinctive characteristics of each modality. These fused representations are subsequently integrated and passed through a lightweight classification head. Extensive experiments on three benchmark RS datasets demonstrate that CAB-HL consistently outperforms existing state-of-the-art models, confirm that CAB-HL consistently outperforms in learning deep joint representations for multimodal classification tasks. Full article

In high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) imaging, the azimuth multi-channel technique effectively suppresses azimuth ambiguity, serving as a reliable approach for achieving wide-swath imaging. However, due to mechanical vibrations of the platform and airflow instabilities, airborne SAR may experience errors in attitude and flight path during operation. Furthermore, errors also exist in the antenna patterns, frequency stability, and phase noise among the azimuth multi-channels. The presence of these errors can cause azimuth multi-channel reconstruction failure, resulting in azimuth ambiguity and significantly degrading the quality of HRWS images. This article presents a novel error correction method for airborne HRWS SAR based on azimuth-variant attitude and range-variant Doppler domain pattern, which simultaneously considers the effects of various errors, including channel attitude errors and Doppler domain antenna pattern errors, on azimuth reconstruction. Attitude errors are the primary cause of azimuth-variant errors between channels. This article uses the vector method and attitude transformation matrix to calculate and compensate for the attitude errors of azimuth multi-channels, and employs the two-dimensional frequency-domain echo interferometry method to calculate the fixed delay errors and fixed phase errors. To better achieve channel error compensation, this scheme also considers the estimation and compensation of Doppler domain antenna pattern errors in wide-swath scenes. Finally, the effectiveness of the proposed scheme is confirmed through simulations and processing of airborne real data. Full article

Most existing deep learning-based super-resolution (SR) methods for remote sensing images rely on predefined degradation assumptions (e.g., bicubic downsampling). However, when real-world degradations deviate from these assumptions, their performance deteriorates significantly. Moreover, explicit degradation estimation approaches based on iterative schemes inevitably lead to accumulated estimation errors and time-consuming processes. In this paper, instead of explicitly estimating degradation types, we first innovatively introduce an MSCN_G coefficient to capture global prior information corresponding to different distortions. Subsequently, distortion-enhanced representations are implicitly estimated through contrastive learning and embedded into a super-resolution network equipped with multiple distortion decoders (D-Decoder). Furthermore, we propose a distortion-related channel segmentation (DCS) strategy that reduces the network’s parameters and computation (FLOPs). We refer to this Global Prior-guided Distortion-enhanced Representation Learning Network as GDRNet. Experiments on both synthetic and real-world remote sensing images demonstrate that our GDRNet outperforms state-of-the-art blind SR methods for remote sensing images in terms of overall performance. Under the experimental condition of anisotropic Gaussian blurring without added noise, with a kernel width of 1.2 and an upscaling factor of 4, the super-resolution reconstruction of remote sensing images on the NWPU-RESISC45 dataset achieves a PSNR of 28.98 dB and SSIM of 0.7656. Full article

Mitochondrial fission and fusion appear to be relatively infrequent in cardiac cells compared to other cell types; however, the proteins involved in these events are highly expressed in adult cardiomyocytes (ACM). Therefore, these proteins likely have additional non-canonical roles. We have previously shown that DRP1 not only participates in mitochondrial fission processes but also regulates mitochondrial bioenergetics in cardiac tissue. However, it is still unknown where the DRP1 that does not participate in mitochondrial fission is located and what its role is at those non-fission spots. Therefore, this manuscript will clarify whether oligomeric DRP1 is located at the SR–mitochondria interface, a specific region that harbors the Ca²⁺ microdomains created by Ca²⁺ release from the SR through the RyR2. The high Ca²⁺ microdomains and the subsequent Ca²⁺ uptake by mitochondria through the mitochondrial Ca²⁺ uniporter complex (MCUC) are essential to regulate mitochondrial bioenergetics during excitation–contraction (EC) coupling. Herein, we aimed to test the hypothesis that mitochondria-bound DRP1 preferentially accumulates at the mitochondria–SR contacts to deploy its function on regulating mitochondrial bioenergetics and that this strategic position is modulated by calcium in a beat-to-beat manner. In addition, the mechanism responsible for such a biased distribution and its functional implications was investigated. High-resolution imaging approaches, cell fractionation, Western blot, 2D blue native gel electrophoresis, and immunoprecipitations were applied to both electrically paced ACM and Langendorff-perfused beating hearts to elucidate the mechanisms of the strategic DRP1 localization. Our data show that in ACM, mitochondria-bound DRP1 clusters in high molecular weight protein complexes at mitochondria-associated membrane (MAM). This clustering requires DRP1 interaction with β-ACTIN and is fortified by EC coupling-mediated Ca²⁺ transients. In ACM, DRP1 is anchored at the mitochondria–SR contacts through interactions with β-ACTIN and Ca²⁺ transients, playing a fundamental role in regulating mitochondrial physiology. Full article

Traditional unmanned aerial vehicle (UAV) path planning methods for image-based 3D reconstruction often rely solely on geometric information from initial models, resulting in redundant data acquisition in non-architectural areas. This paper proposes a UAV path planning method via semantic segmentation of 3D reality mesh models to enhance efficiency and accuracy in complex scenarios. The scene is segmented into buildings, vegetation, ground, and water bodies. Lightweight polygonal surfaces are extracted for buildings, while planar segments in non-building regions are fitted and projected into simplified polygonal patches. These photography targets are further decomposed into point, line, and surface primitives. A multi-resolution image acquisition strategy is adopted, featuring high-resolution coverage for buildings and rapid scanning for non-building areas. To ensure flight safety, a Digital Surface Model (DSM)-based shell model is utilized for obstacle avoidance, and sky-view-based Real-Time Kinematic (RTK) signal evaluation is applied to guide viewpoint optimization. Finally, a complete weighted graph is constructed, and ant colony optimization is employed to generate a low-energy-cost flight path. Experimental results demonstrate that, compared with traditional oblique photogrammetry, the proposed method achieves higher reconstruction quality. Compared with the commercial software Metashape, it reduces the number of images by 30.5% and energy consumption by 37.7%, while significantly improving reconstruction results in both architectural and non-architectural areas. Full article

Computer vision-based part surface inspection is widely used for quality evaluation. However, challenges such as low image quality, caused by factors like inadequate acquisition equipment, camera vibrations, and environmental conditions, often lead to reduced detection accuracy. Although super-resolution reconstruction can enhance image quality, existing methods face issues such as limited accuracy, information distortion, and high computational cost. To overcome these challenges, we propose a novel super-resolution reconstruction method for part images that incorporates adaptive multi-scale object tracking. Our approach first adaptively segments the input sequence of part images into blocks of varying scales, improving both reconstruction accuracy and computational efficiency. Optical flow is then applied to estimate the motion parameters between sequence images, followed by the construction of a feature tracking and sampling model to extract detailed features from all images, addressing information distortion caused by pixel misalignment. Finally, a non-linear reconstruction algorithm is employed to generate the high-resolution target image. Experimental results demonstrate that our method achieves superior performance in terms of both quantitative metrics and visual quality, outperforming existing methods. This contributes to a significant improvement in subsequent part detection accuracy and production efficiency. Full article

Background/Objectives: Alterations in liver vascularization play a remarkable role in liver disease development, including hepatocellular carcinoma (HCC), but remain understudied. This study evaluated the hepatic microvascular imaging method and provided high-resolution quantitative anatomical data on the characteristics and architecture of liver vasculature in wild-type (WT) mice and HCC mouse models. Methods: C57BL/6 mice were injected with Akt/Ras or Sleeping Beauty transposon to induce HCC. Liver tissues from normal and Akt/Ras mice underwent hematoxylin and eosin, Masson’s trichrome, Ki67, and lymphatic endothelial receptor-1 staining. Using cutting-edge high-definition fluorescence micro-optical sectioning tomography, high-precision microvascular visualization of the liver was performed in WT and Akt/Ras HCC mice. Results: The sectioned volumes of normal and HCC liver tissues were 204.8 mm³ and 212.8 mm³, respectively. The microvascular systems associated with the tissues of the Akt/Ras HCC mouse were twisted, disordered, and compressed by tumor nodules. In the four tumor nodules, the path of the hepatic artery was more around the tumor edge, whereas the portal vein occupied the central position and constituted the main blood vessel entering the tumors. The porosity of HCC and paracancerous cirrhotic tissues was significantly less than that of normal tissues. The radii of the central vessels in the hepatic sinusoid of paratumoral cirrhotic tissues were significantly higher than those of normal tissues; however, the hepatic sinusoid density of paratumoral cirrhotic tissues was lower. Conclusions: This research provides a deeper understanding of the normal liver microvasculature and alterations in cases of cirrhosis and HCC, which complements scientific insights into liver morphology and physiology. This straightforward research approach involving the novel 3D liver microvasculature can be used in multiscale physiological and pathophysiological studies regarding liver diseases. Full article