Search Results (50)

Accurate monitoring of rice-planting areas underpins food security and evidence-based farm management. Recent work has advanced along three complementary lines—multi-source data fusion (to mitigate cloud and spectral confusion), temporal feature extraction (to exploit phenology), and deep-network architecture optimization. However, even the best fusion- and time-series-based approaches still struggle to preserve fine spatial details in sub-meter scenes. Targeting this gap, we propose an HRNet-CA-enhanced DeepLabV3+ that retains the original model’s strengths while resolving its two key weaknesses: (i) detail loss caused by repeated down-sampling and feature-pyramid compression and (ii) boundary blurring due to insufficient multi-scale information fusion. The Xception backbone is replaced with a High-Resolution Network (HRNet) to maintain full-resolution feature streams through multi-resolution parallel convolutions and cross-scale interactions. A coordinate attention (CA) block is embedded in the decoder to strengthen spatially explicit context and sharpen class boundaries. The rice dataset consisted of 23,295 images (11,295 rice + 12,000 non-rice) via preprocessing and manual labeling and benchmarked the proposed model against classical segmentation networks. Our approach boosts boundary segmentation accuracy to 92.28% MIOU and raises texture-level discrimination to 95.93% F1, without extra inference latency. Although this study focuses on architecture optimization, the HRNet-CA backbone is readily compatible with future multi-source fusion and time-series modules, offering a unified path toward operational paddy mapping in fragmented sub-meter landscapes. Full article

As China’s electricity consumption surges, the reliability and safety of long-distance transmission lines become increasingly crucial. Insulators, vital for grid stability, demand accurate defect identification. Existing methods fall short on small targets and complex backgrounds. An insulator defect detection method MRB-YOLOv8 is proposed. By integrating an attention mechanism and multi-scale features, the model’s focus on key features is significantly improved. The Multi-Spectral Channel Attention captures essential information across different frequency domains through a well-designed frequency selection strategy. In addition, Receptive Field Attention Convolution (RFAConv) replaces the C2f module in the backbone network, which enhances the ability to perceive the features in complex backgrounds through the weighting operation of the receptive field weights. Meanwhile, the weighted bi-directional feature pyramid network (BiFPN) and a fourth detection layer prevent feature loss during fusion, enhancing the detection accuracy of small targets. Experimental results show that, at mAP50 and mAP50:95, the improved method obtains a gain of 3.2% and 3.6%, respectively, which significantly improves the model’s capability of detecting defects such as insulator self-explosion, breakage, and flashover in the images captured by UAVs. Full article

Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. Due to the short and sparse features of shrubs, capturing the shrub biomass accurately by satellite observations is challenging. Previous studies mostly used ground samples and satellite observations to estimate shrub biomass by establishing a direct connection, which was often hindered by the limited number of ground samples and spatial scale mismatch between samples and observations. Unmanned aerial vehicles (UAVs) provide opportunities to obtain more samples that are in line with the aspects of satellite observations (i.e., scale) for regional-scale shrub biomass estimations accurately with low costs. However, few studies have been conducted based on the air-space-ground-scale connection assisted by UAVs. Here we developed a framework for estimating 10 m shrub biomass at a regional scale by integrating ground measurements, UAV, Landsat, and Sentinel-1/2 observations. First, the spatial distribution map of shrublands and non-shrublands was generated in 2023 in the Helan Mountains of Ningxia province, China. This map had an F1 score of 0.92. Subsequently, the UAV-based shrub biomass map was estimated using an empirical model between the biomass and the crown area of shrubs, which was aggregated at a 10 m × 10 m grid to match the spatial resolution of Sentinel-1/2 images. Then, a regional-scale estimation model of shrub biomass was developed with a random forest regression (RFR) approach driven by ground biomass measurements, UAV-based biomass, and the optimal satellite metrics. Finally, the developed model was used to produce the biomass map of shrublands over the study area in 2023. The uncertainty of the resultant biomass map was characterized by the pixel-level standard deviation (SD) using the leave-one-out cross-validation (LOOCV) method. The results suggested that the integration of multi-scale observations from the ground, UAVs, and satellites provided a promising approach to obtaining the regional shrub biomass accurately. Our developed model, which integrates satellite spectral bands and vegetation indices (R² = 0.62), outperformed models driven solely by spectral bands (R² = 0.33) or vegetation indices (R² = 0.55). In addition, our estimated biomass has an average uncertainty of less than 4%, with the lowest values (<2%) occurring in regions with high shrub coverage (>30%) and biomass production (>300 g/m²). This study provides a methodology to accurately monitor the shrub biomass from satellite images assisted by near-ground UAV observations as well as ground measurements. Full article

The estimation of water quality properties through satellite remote sensing relies on (1) the optical characteristics of the water body, (2) the resolutions (spatial, spectral, radiometric and temporal) of the sensor and (3) algorithm(s) applied. More than 80% of global water bodies fall under Case I (open ocean) waters, dominated by scattering and absorption associated with phytoplankton in the water column. Globally, previous studies show significant correlations between satellite-based retrieval methods and field measurements of absorbing and scattering constituents, while limited research from Australian coastal water bodies appears. This study presents a methodology to extract chlorophyll a properties from surface waters from near-coastal environments, within 2 km of coastline, in Tasmania, south-eastern Australia. We use general purpose, global, long-time series, multi-spectral satellite data, as opposed to ocean colour-specific sensor data. This approach may offer globally applicable tools for combining global satellite image archives with in situ field sensors for water quality monitoring. To enable applications from local to global scales, a cloud-based geospatial analysis workflow was developed and tested on several sites. This work represents the initial stage in developing a semi-automated near-coastal water-quality workflow using easily accessed, fully corrected global multi-spectral datasets alongside large-scale computation and delivery capabilities. Our results indicated a strong correlation between the in situ chlorophyll concentration data and blue-green band ratios from the multi-spectral sensor. In line with published research, environment-specific empirical models exhibited the highest correlations between in situ and satellite measurements, underscoring the importance of tailoring models to specific coastal waters. Our findings may provide the basis for developing this workflow for other sites in Australia. We acknowledge the use of general purpose multi-spectral data such as the Sentinel-2 and Landsat Series, their corrections and algorithms may not be as accurate and precise as ocean colour satellites. The data we are using are more readily accessible and also have true global coverage with global historic archives and regular, global collection will continue at least 10 years in the future. Regardless of sensor specifications, the retrieval method relies on localised algorithm calibration and validation using in situ measurements, which demonstrates close-to-realistic outputs. We hope this approach enables future applications to also consider these globally accessible and regularly updated datasets that are suited to coastal environments. Full article

Over the preceding five decades, there has been a noticeable surge in the pursuit of achieving voluminous and well-defined lips. This trend has prompted an escalating number of individuals to undergo lip augmentation procedures, aiming for a natural three-dimensional enhancement of lip volume and distinct vermilion borders. Despite the proliferation of lip augmentation techniques, there remains a dearth of comprehensive investigations into their precise effects on the three-dimensional structural integrity of the lips. This research endeavors to address this gap by employing stereophotogrammetry as a quantitative tool to scrutinize lip augmentation outcomes and to appraise the efficacy of lip plumpers. The study methodology involves a comparative analysis of lip dimensions among subjects treated with a commercial lip plumper using multi-spectral imaging for lip dimension assessment, coupled with markerless tracking technology and 3D interpolating surface methodology to analyze lip volume and shape. Additionally, the study evaluated lip youth state, including moisture level, softness, firmness, and tissue density. The demand for lip augmentation procedures is driven by perceived advantages such as quick recovery and minimal risk. Therefore, it is crucial to substantiate their efficacy with robust findings. The investigation suggests that both 3D and 2D stereophotogrammetry techniques are reliable for evaluating lip size before and after augmentation, whether through cosmetic or aesthetic approaches. Overall, the study provides a comprehensive analysis of a lip treatment aimed at enhancing volume and redesigning lip lines. It demonstrates that stereophotogrammetry is effective for assessing 3D lip dimensions and their correlation with internal lip structure. This research could be particularly valuable for evaluating the efficacy and duration of various lip enhancement techniques, including dermal fillers, implants, and topical cosmetic formulations, offering quantitative and reproducible assessments over time. Full article

Based on the application requirements of high spectral resolutions, high spatial resolutions and wide swatches, a new-generation, high-performance, spaceborne, hyperspectral imaging spectrometer (NGHSI) with a spatial resolution of 15 m and a swatch of 90 km is proposed. The optical system of the NGHSI has a focal length of 1128 mm, an F-number of three, a field of view (FOV) of 7.32° and a slit length of 144 mm. A new off-axis, multi-mirror telescope structure with intermediate images is put forward, which solves the design problem that realizes secondary imaging and good telecentricity at the same time. And a new off-axis lens-compensation Offner configuration is adopted to address the challenge of the high-fidelity design of spectral imaging systems with long slit lengths. The relationship between X-Y polynomials and aberration coefficients is analyzed, and the X-Y polynomial freeform surfaces are used to correct the off-axis aberrations. The design results show that the image quality of the telescope system is close to the diffraction limit. The smile, known as the spectral distortion along the line, and keystone, which is the magnification difference for different wavelengths, of the spectral imaging system are less than 1/10 pixel size. The complete optical system of the NGHSI, including the telescope system and the spectral imaging system, has excellent imaging quality and the layout is compact and reasonable, which realizes the miniaturization design. Full article

Hyperspectral sensors mounted on unmanned aerial vehicles (UAV) offer the prospect of high-resolution multi-temporal spectral analysis for a range of remote-sensing applications. However, although accurate onboard navigation sensors track the moment-to-moment pose of the UAV in flight, geometric distortions are introduced into the scanned data sets. Consequently, considerable time-consuming (user/manual) post-processing rectification effort is generally required to retrieve geometrically accurate mosaics of the hyperspectral data cubes. Moreover, due to the line-scan nature of many hyperspectral sensors and their intrinsic inability to exploit structure from motion (SfM), only 2D mosaics are generally created. To address this, we propose a fast, automated and computationally robust georectification and mosaicking technique that generates 3D hyperspectral point clouds. The technique first morphologically and geometrically examines (and, if possible, repairs) poorly constructed individual hyperspectral cubes before aligning these cubes into swaths. The luminance of each individual cube is estimated and normalised, prior to being integrated into a swath of images. The hyperspectral swaths are co-registered to a targeted element of a luminance-normalised orthomosaic obtained using a standard red–green–blue (RGB) camera and SfM. To avoid computationally intensive image processing operations such as 2D convolutions, key elements of the orthomosaic are identified using pixel masks, pixel index manipulation and nearest neighbour searches. Maximally stable extremal regions (MSER) and speeded-up robust feature (SURF) extraction are then combined with maximum likelihood sample consensus (MLESAC) feature matching to generate the best geometric transformation model for each swath. This geometrically transforms and merges individual pushbroom scanlines into a single spatially continuous hyperspectral mosaic; and this georectified 2D hyperspectral mosaic is then converted into a 3D hyperspectral point cloud by aligning the hyperspectral mosaic with the RGB point cloud used to create the orthomosaic obtained using SfM. A high spatial accuracy is demonstrated. Hyperspectral mosaics with a 5 cm spatial resolution were mosaicked with root mean square positional accuracies of 0.42 m. The technique was tested on five scenes comprising two types of landscape. The entire process, which is coded in MATLAB, takes around twenty minutes to process data sets covering around 30 Ha at a 5 cm resolution on a laptop with 32 GB RAM and an Intel^® Core i7-8850H CPU running at 2.60 GHz. Full article

In order to capture true-color information of distant targets under extremely low light, a large-format low-light imaging system is designed based on an RGB filter wheel. By decomposing the system indicators, this study proposes a method for acquiring low-light true-color images using a large-aperture, low-distortion optical lens combined with an RGB filter wheel capable of multi-line sequential exposure. The optical field segmentation is achieved using a four-panel optical reflective prism, and the images from four high-sensitivity SCOMS detectors are stitched together to form a composite image. The working principle of the system is explained, and the low-light imaging capability is thoroughly evaluated. The dimensions and rotation speed of the filter wheel are then calculated in detail, ensuring accurate synchronization of the filter wheel’s speed and exposure time. The calculation method for the parameters of the four-panel reflective prism structure is investigated, mathematical expressions for the geometric parameters of the prism assembly are provided, and a prism assembly suitable for four-way spectral separation is designed. Based on the research and design results, a large-swath-width, low-light true-color imaging system is developed that is suitable for an environmental illuminance of 0.01 lux. The system achieves a ground pixel resolution of 0.5 m (at an altitude of 5 km) and an effective image resolution of 4 K × 4 K, and is capable of accurately reproducing target color information. Laboratory and field flight tests verified that the large-swath-width images obtained by the imaging system are clear, with high contrast and resolution. After image fusion and spectral registration, the color images exhibit full saturation and high fidelity, meeting the requirements of low-light true-color imaging under airborne conditions. The design methodology of this low-light imaging system can serve as a reference for the development of airborne low-light imaging equipment. Full article

The multispectral imager (MII), onboard the Sustainable Development Science Satellite 1 (SDGSAT-1), performs detailed terrestrial change detection and coastal monitoring. SDGSAT-1 was launched at 2:19 UTC on 5 November 2021, as the world’s first Earth science satellite to serve the United Nations 2030 Sustainable Development Agenda. A vicarious radiometric calibration experiment was conducted at the Dunhuang calibration site (Gobi Desert, China) on 14 December 2021. In-situ measurements of ground reflectance, aerosol optical depth (AOD), total columnar water vapor, radiosonde data, and diffuse-to-global irradiance (DG) ratio were performed to predict the top-of-atmosphere radiance by the reflectance-, irradiance-, and improved irradiance-based methods using the moderate resolution atmospheric transmission model. The MII calibration coefficients were calculated by dividing the top-of-atmosphere radiance by the average digital number value of the image. The radiometric calibration coefficients calculated by the three calibration methods were reliable (average relative differences: 2.20% (reflectance-based vs. irradiance-based method) and 1.43% (reflectance-based vs. improved irradiance-based method)). The total calibration uncertainties of the reflectance-, irradiance-, and improved irradiance-based methods were 2.77–5.23%, 3.62–5.79%, and 3.50–5.23%, respectively. The extra DG ratio measurements in the latter two methods did not improve the calibration accuracy for AODs ≤ 0.1. The calibrated MII images were verified using Landsat-8 Operational Land Imager (OLI) and Sentinel-2A MultiSpectral Instrument (MSI) images. The retrieved ground reflectances of the MII over different surface types were cross-compared with those of OLI and MSI using the FAST Line-of-sight Atmospheric Analysis of Hypercubes software. The MII retrievals differed by <0.0075 (7.13%) from OLI retrievals and <0.0084 (7.47%) from MSI retrievals for calibration coefficients from the reflectance-based method; <0.0089 (7.57%) from OLI retrievals and <0.0111 (8.65%) from MSI retrievals for the irradiance-based method; and <0.0082 (7.33%) from OLI retrievals and <0.0101 (8.59%) from MSI retrievals for the improved irradiance-based method. Thus, our findings support the application of SDGSAT-1 data. Full article

Heterogeneous three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is an important but very challenging technique for recovering the conformational heterogeneity of flexible biological macromolecules such as proteins in different functional states. Heterogeneous projection image classification is a feasible solution to solve the structural heterogeneity problem in single-particle cryo-EM. The majority of heterogeneous projection image classification methods are developed using supervised learning technology or require a large amount of a priori knowledge, such as the orientations or common lines of the projection images, which leads to certain limitations in their practical applications. In this paper, an unsupervised heterogeneous cryo-EM projection image classification algorithm based on autoencoders is proposed, which only needs to know the number of heterogeneous 3D structures in the dataset and does not require any labeling information of the projection images or other a priori knowledge. A simple autoencoder with multi-layer perceptrons trained in iterative mode and a complex autoencoder with residual networks trained in one-pass learning mode are implemented to convert heterogeneous projection images into latent variables. The extracted high-dimensional features are reduced to two dimensions using the uniform manifold approximation and projection dimensionality reduction algorithm, and then clustered using the spectral clustering algorithm. The proposed algorithm is applied to two heterogeneous cryo-EM datasets for heterogeneous 3D reconstruction. Experimental results show that the proposed algorithm can effectively extract category features of heterogeneous projection images and achieve high classification and reconstruction accuracy, indicating that the proposed algorithm is effective for heterogeneous 3D reconstruction in single-particle cryo-EM. Full article

Particle size distribution is an important characteristic of reclaimed soil in arid and semi-arid mining areas in western China, which is important in the ecological environment protection and control of the Yellow River Basin. Large-scale coal resource mining disturbances have caused serious damage to the fragile ecological environment. The timely and accurate dynamic monitoring of mining area topsoil information has practical significance for ecological restoration and management evaluation. Investigating Wuhai City in the Inner Mongolia Autonomous Region of China, this study uses Landsat8 OLI multispectral images and measured soil sample particle size data to analyze soil spectral characteristics and establish a particle size content prediction model to retrieve the particle size distribution in the study area. The experimental results and analysis demonstrate that: (1) the 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum Vector version) atmospheric correction model is more accurate than the FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes) model in arid and semi-arid areas with undulating terrain; (2) 0–40 cm is the optimum soil thickness for modeling and predicting particle size content in this study; and (3) the multi-band prediction model is more precise than the single-band prediction model. The multi-band model’s sequence of advantages and disadvantages is SVM (Support Vector Machine) > MLR (Multiple Linear Regression) > PLSR (Partial Least Squares Regression). Among them, the 6SV-SVM model has the highest precision, and the prediction precision R² of the 3 particle sizes’ contents is above 0.95, which can effectively predict the soil particle-size distribution and provide effective data to support topsoil quality change monitoring in the mine land reclamation area. Full article

We present a case for significantly enhancing the utility and efficiency of the ngEHT by incorporating an additional 86 GHz observing band. In contrast to 230 or 345 GHz, weather conditions at the ngEHT sites are reliably good enough for 86 GHz to enable year-round observations. Multi-frequency imaging that incorporates 86 GHz observations would sufficiently augment the $(u,v)$ coverage at 230 and 345 GHz to permit detection of the M87 jet structure without requiring EHT stations to join the array. The general calibration and sensitivity of the ngEHT would also be enhanced by leveraging frequency phase transfer techniques, whereby simultaneous observations at 86 GHz and higher-frequency bands have the potential to increase the effective coherence times from a few seconds to tens of minutes. When observation at the higher frequencies is not possible, there are opportunities for standalone 86 GHz science, such as studies of black hole jets and spectral lines. Finally, the addition of 86 GHz capabilities to the ngEHT would enable it to integrate into a community of other VLBI facilities—such as the GMVA and ngVLA—that are expected to operate at 86 GHz but not at the higher ngEHT observing frequencies. Full article

Precision cancer medicine necessitates a personalized treatment plan for each individual patient. Given cancer’s heterogeneity and dynamic nature, the plot of patient-specific signatures composed of multiple cancer circulating biomarkers is useful to reveal the complete tumor landscape for guiding precision medicine. As an emerging new technology, surface-enhanced Raman scattering (SERS) shows the intrinsic advantage of performing multiplexed detection with the extremely narrow Raman spectral line widths. In this review, we first discuss the design principle of SERS nanotags to enable the detection of multiple circulating biomarkers, highlighting the important roles of plasmonic nanostructures and triple bond-modulated Raman reporters. Following this, we detail the use of isotropic and anisotropic nanostructures as SERS enhancement substrates for amplifying Raman signals in multi-biomarker detection. Furthermore, we present the triple bond-modulated molecules as Raman reporters in SERS nanotags to expand the multiplexing capability for biomarker measurements. Finally, we offer critical insights into the challenges and perspectives of SERS nanotags for cancer diagnosis, particularly from the aspect of future clinical transition. It is expected that this review can facilitate the design of more functional SERS nanotags with high sensitivity and multiplexing capability to assist early and accurate cancer screening. We also believe our review will be of interest in the fields of molecular imaging, biomedicine, and analytical chemistry. Full article

Remote sensing is increasingly recognized as a convenient tool with a wide variety of uses in agriculture. Landsat-7 has supplied multi-spectral imagery of the Earth’s surface for more than 4 years and has become an important data source for a large number of research and policy-making initiatives. Unfortunately, a scan line corrector (SLC) on Landsat-7 broke down in May 2003, which caused the loss of up to 22 percent of any given scene. We present a single-image approach based on leveraging the abilities of the deep image prior method to fill in gaps using only the corrupt image. We test the ability of deep image prior to reconstruct remote sensing scenes with different levels of corruption in them. Additionally, we compare the performance of our approach with the performance of classical single-image gap-filling methods. We demonstrate a quantitative advantage of the proposed approach compared with classical gap-filling methods. The lowest-performing restoration made by the deep image prior approach reaches 0.812 in $r^2$, while the best value for the classical approaches is 0.685. We also present the robustness of deep image prior in comparing the influence of the number of corrupted pixels on the restoration results. The usage of this approach could expand the possibilities for a wide variety of agricultural studies and applications. Full article

Microplastic particles produced by non-degradable waste plastic bottles have a critical impact on the environment. Reasonable recycling is a premise that protects the environment and improves economic benefits. In this paper, a multi-scale feature fusion method for RGB and hyperspectral images based on Segmenting Objects by Locations (RHFF-SOLOv1) is proposed, which uses multi-sensor fusion technology to improve the accuracy of identifying transparent polyethylene terephthalate (PET) bottles, blue PET bottles, and transparent polypropylene (PP) bottles on a black conveyor belt. A line-scan camera and near-infrared (NIR) hyperspectral camera covering the spectral range from 935.9 nm to 1722.5 nm are used to obtain RGB and hyperspectral images synchronously. Moreover, we propose a hyperspectral feature band selection method that effectively reduces the dimensionality and selects the bands from 1087.6 nm to 1285.1 nm as the features of the hyperspectral image. The results show that the proposed fusion method improves the accuracy of plastic bottle classification compared with the SOLOv1 method, and the overall accuracy is 95.55%. Finally, compared with other space-spectral fusion methods, RHFF-SOLOv1 is superior to most of them and achieves the best (97.5%) accuracy in blue bottle classification. Full article