Search Results (71)

The imaging quality of uncooled long wave infrared (IR) cameras is always limited by the stripe non-uniformity mainly caused by fixed pattern noise (FPN). In this paper, we propose an adaptive dual-frequency denoising network-based stripe non-uniformity correction (NUC) method, namely ADFDNet, to realize the balance between FPN removal and image detail preservation. Our ADFDNet takes the dual-frequency feature deconstruction module as its core, which decomposes the IR image into high-frequency and low-frequency features, and performs targeted processing through detail enhancement branches and sparse denoising branches. The former enhances the performance of detail preservation through multi-scale convolution and pixel attention mechanism, while the latter combines sparse attention mechanism and dilated convolution design to suppress high-frequency FPN. Furthermore, the dynamic weight fusion of features is realized using the adaptive dual-frequency fusion module, which better integrates detail information. In our study, a 420-pair image dataset covering different noise levels is constructed for better model training and evaluation. Experiments verify that the presented ADFDNet method significantly improves image clarity in both real and simulated noise scenes, and achieves a better balance between FPN suppression and detail preservation than other existing methods. Full article

The photovoltaic (PV) bands on the mid-wave and long-wave infrared (MWIR and LWIR) cold focal plane assemblies of Terra and Aqua MODIS have suffered from gradually increasing electronic crosstalk contamination as both instruments have continued to operate in their extended missions, respectively. This contamination has considerable impact, particularly for the PV LWIR bands, which includes image striping and radiometric bias in the Level-1B (L1B)-calibrated radiance products as well as higher level (and mostly atmospheric but also land and oceanic) products (e.g., cloud phase particle, cloud mask, land and sea surface temperatures). The crosstalk was characterized early in the mission, and test corrections were developed then. Ultimately, the groundwork for a robust electronic crosstalk correction algorithm was developed in 2016 and implemented in MODIS Collection 6.1 (C6.1) back in 2017 for the Terra MODIS PV LWIR bands. It was later introduced in Aqua MODIS C6.1 for the same group of bands in April 2022. Additional improvements were made in MODIS Collection 7 (C7) to better characterize the electronic crosstalk in the PV LWIR bands, and the electronic crosstalk correction algorithm was also extended to select detectors in the MODIS MWIR bands. This work will describe the electronic crosstalk correction algorithm and its application on the MODIS L1B product, the differences in application between C6.1 and C7, as well as additional improvements made to enhance the contamination correction and improve image quality for the Aqua MODIS PV LWIR bands. The electronic crosstalk correction coefficient time series for the MODIS PV bands will be discussed, and some cases will be presented to illustrate how image quality improves on the L1B and Level 2 products after the correction is applied. Lastly, experiences gained regarding the PV bands electronic crosstalk and the strategy used to correct it will be discussed to provide future data users and scientists with an insight as to how to improve on the legacy record that the Terra and Aqua MODIS sensors will leave behind after both spacecrafts are decommissioned. Full article

Addressing the difficulty of intuitively identifying and effectively correcting induced heave error in multibeam measurements, this paper proposes a two-stage methodology comprising error identification and correction. This scheme includes an error discrimination method based on regression diagnostics and an error correction method based on Partial Least Squares Regression (PLSR). By establishing a mathematical model between bathymetric discrepancies and attitude parameters, statistical diagnosis and effective identification of the error are achieved. To further mitigate the impact of induced heave error on bathymetric data, an elimination model based on PLSR is developed, enabling high-precision prediction and compensation of the induced heave error. Validation using field survey data demonstrates that this method can effectively estimate the installation offset parameters of the attitude sensor. After correction, the root mean square of bathymetric discrepancies between adjacent survey lines is reduced by approximately 78.8%, periodic stripe-shaped distortions along the track direction are essentially eliminated, and the quality of terrain mosaicking is significantly improved. This provides an effective solution for controlling induced heave error under complex topographic conditions. Full article

Traditional water quality monitoring methods are limited in providing timely chlorophyll-a (Chl-a) assessments in small inland reservoirs. This study presents a rapid Chl-a retrieval approach based on a cooperative unmanned aerial vehicle–uncrewed surface vessel (UAV–USV) framework that integrates UAV hyperspectral imaging, machine learning algorithms, and synchronized USV in situ sampling. We carried out a three-day cooperative monitoring campaign in the Longhu Reservoir of Fujian Province, during which high-frequency hyperspectral imagery and water samples were collected. An innovative median-based correction method was developed to suppress striping noise in UAV hyperspectral data, and a two-step band selection strategy combining correlation analysis and variance inflation factor screening was used to determine the input features for the subsequent inversion models. Four commonly used machine-learning-based inversion models were constructed and evaluated, with the random forest model achieving the highest accuracy and stability across both training and testing datasets. The generated Chl-a maps revealed overall good water quality, with localized higher concentrations in weakly hydrodynamic zones. Overall, the cooperative UAV–USV framework enables synchronized data acquisition, rapid processing, and fine-scale mapping, demonstrating strong potential for fast-response and emergency water-quality monitoring in small inland drinking-water reservoirs. Full article

Hyperspectral data from CRISM have proven invaluable for analyzing the mineralogical composition of the Martian surface. However, processing such datasets remains challenging due to their high dimensionality and systematic noise, such as striping artifacts caused by the pushbroom imaging technique. Building on previous research, this study introduces a framework that forms the basis for an automated pipeline that combines preprocessing, dimensionality reduction using UMAP, k-means clustering, and an adaptive stripe correction filter to generate mineral maps of the Martian surface. Additionally, the pipeline integrates a noise variance estimation step based on PCA to assess the feasibility and expected efficacy of stripe removal before applying the filter. We validate the methodology across multiple CRISM datasets, including regions such as Jezero Crater, Nili Fossae, and Mawrth Vallis. Comparative analyses using metrics such as the CH index, DB index, and SC demonstrate improved clustering performance and robust mineralogical mapping, which indicates a step toward more reliable and automated clustering of CRISM data. Furthermore, the pipeline leverages spectral libraries for automated mineral classification, yielding results comparable to expert-defined maps while addressing discrepancies caused by residual noise or clustering limitations. This study represents a step toward fully automated, scalable geospatial analysis of CRISM Martian surface data, offering a robust framework for processing large hyperspectral datasets and supporting future planetary exploration missions. In the future, we intend to deploy an automated analysis pipeline as a freely accessible web service. Full article

The detection of citrus separation lines is a crucial step in the citrus processing industry. Inspired by the achievements of line-structured light technology in surface defect detection, this paper proposes a method for detecting citrus separation lines based on line-structured light. Firstly, a gamma-corrected Otsu method is employed to extract the laser stripe region from the image. Secondly, an improved skeleton extraction algorithm is employed to mitigate the bifurcation errors inherent in original skeleton extraction algorithms while simultaneously acquiring 3D point cloud data of the citrus surface. Finally, the least squares progressive iterative approximation algorithm is applied to approximate the ideal surface curve; subsequently, principal component analysis is used to derive the normals of this ideally fitted curve. The deviation between each point (along its corresponding normal direction) and the actual geometric characteristic curve is then adopted as a quantitative index for separation lines positioning. The average similarity between the extracted separation lines and the manually defined standard separation lines reaches 92.5%. In total, 95% of the points on the separation lines obtained by this method have an error of less than 4 pixels. Experimental results demonstrate that through quantitative deviation analysis of geometric features, automatic detection and positioning of the separation lines are achieved, satisfying the requirements of high precision and non-destructiveness for automatic citrus splitting. Full article

Stripe noise along the scanning direction significantly degrades the quality of high-resolution infrared line-scan images and impairs downstream tasks such as target detection and radiometric analysis. This paper presents a lightweight, single-frame, reference-free non-uniformity correction (NUC) method tailored for such images. The proposed approach enhances the directionality of stripe noise by projecting the 2D image into a 1D row-mean signal, followed by adaptive guided filtering driven by local median absolute deviation (MAD) to ensure spatial adaptivity and structure preservation. A spectral-entropy-constrained frequency-domain masking strategy is further introduced to suppress periodic and non-periodic interference. Extensive experiments on simulated and real datasets demonstrate that the method consistently outperforms six state-of-the-art algorithms across multiple metrics while maintaining the fastest runtime. The proposed method is highly suitable for real-time deployment in airborne, satellite-based, and embedded infrared imaging systems. It provides a robust and interpretable framework for future infrared enhancement tasks. Full article

Horizontal curves have been a significant safety concern on roads for years, often resulting in a high incidence of crashes. A European Road Safety Observatory report indicated that 53% of road crashes in the EU in 2020 occurred on rural roads, mainly due to misjudging when navigating these curves. This study explores innovative low-cost road designs for this issue, such as the red-white pattern edge line (RWE), the solid red edge line (RE), the alternating red-white checkered median stripe (RWM), and the red dragon’s teeth (RDT) to improve driver behavior around curves. The various road markings were tested based on speed, acceleration/deceleration, and lateral position before and during horizontal curves in a driving simulator using STISIM Drive^® 3. Fifty-two volunteers, aged between 20 and 75, participated in the study. The simulation road was designed according to the Flemish Road Agency (AWV) guidelines. The simulation tested twelve horizontal curves, including left and right turns, with 125 m and 350 m radii. The results were analyzed using within-subjects repeated measures ANOVA, with Greenhouse–Geisser correction for sphericity violations. It was revealed that these markings can reduce driving speeds and improve control, enhancing road safety. Specifically, the red-white median stripe resulted in better lateral positioning. At the same time, red dragon’s teeth minimized deceleration before curves, although their effects were less significant for curves with larger radii. Full article

The inhomogeneity of spectral pixel response is an unavoidable phenomenon in hyperspectral imaging, which is mainly manifested by the existence of inhomogeneity banding noise in the acquired hyperspectral data. It must be carried out to get rid of this type of striped noise since it is frequently uneven and densely distributed, which negatively impacts data processing and application. By analyzing the source of the instrument noise, this work first created a novel non-uniform noise removal method for a spatial dimensional push sweep hyperspectral imaging system. Clean and clear medical hyperspectral brain tumor tissue images were generated by combining scene-based and reference-based non-uniformity correction denoising algorithms, providing a strong basis for further diagnosis and classification. The precise procedure entails gathering the reference dark background image for rectification and the actual medical hyperspectral brain tumor image. The original hyperspectral brain tumor image is then smoothed using a weighted least squares algorithm model embedded with bilateral filtering (BLF-WLS), followed by a calculation and separation of the instrument fixed-mode fringe noise component from the acquired reference dark background image. The purpose of eliminating non-uniform fringe noise is achieved. In comparison to other common image denoising methods, the evaluation is based on the subjective effect and unreferenced image denoising evaluation indices. The approach discussed in this paper, according to the experiments, produces the best results in terms of the subjective effect and unreferenced image denoising evaluation indices (MICV and MNR). The image processed by this method has almost no residual non-uniform noise, the image is clear, and the best visual effect is achieved. It can be concluded that different denoising methods designed for different noises have better denoising effects on hyperspectral images. The non-uniformity denoising method designed in this paper based on a spatial dimension push-sweep hyperspectral imaging system can be widely used. Full article

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the National Oceanic and Atmospheric Administration-20 (NOAA-20) satellite was launched on 18 November 2017. The on-orbit calibration of the NOAA-20 VIIRS visible and near-infrared (VisNIR) bands has been very stable over time. However, NOAA-20 operational M1 (a dual gain band with a center wavelength of 0.41 µm) sensor data records (SDR) have exhibited persistent scene-dependent striping over clear-sky ocean (high gain, low radiance) since the beginning of the mission, different from other VisNIR bands. This paper studies the root causes of the striping in the operational NOAA-20 M1 SDRs. Two potential factors were analyzed: (1) polarization effect-induced striping over clear-sky ocean and (2) imperfect on-orbit radiometric calibration-induced striping. NOAA-20 M1 is more sensitive to the polarized lights compared to other NOAA-20 short-wavelength bands and the similar bands on the Suomi NPP and NOAA-21 VIIRS, with detector and scan angle-dependent polarization sensitivity up to ~6.4%. The VIIRS M1 top of atmosphere radiance is dominated by Rayleigh scattering over clear-sky ocean and can be up to ~70% polarized. In this study, the impact of the polarization effect on M1 striping was investigated using radiative transfer simulation and a polarization correction method similar to that developed by the NOAA ocean color team. Our results indicate that the prelaunch-measured polarization sensitivity and the polarization correction method work well and can effectively reduce striping over clear-sky ocean scenes by up to ~2% at near nadir zones. Moreover, no significant change in NOAA-20 M1 polarization sensitivity was observed based on the data analyzed in this study. After the correction of the polarization effect, residual M1 striping over clear-sky ocean suggests that there exists half-angle mirror (HAM)-side and detector-dependent striping, which may be caused by on-orbit radiometric calibration errors. HAM-side and detector-dependent striping correction factors were analyzed using deep convective cloud (DCC) observations (low gain, high radiances) and verified over the homogeneous Libya-4 desert site (low gain, mid-level radiance); neither are significantly affected by the polarization effect. The imperfect on-orbit radiometric calibration-induced striping in the NOAA operational M1 SDR has been relatively stable over time. After the correction of the polarization effect, the DCC-based striping correction factors can further reduce striping over clear-sky ocean scenes by ~0.5%. The polarization correction method used in this study is only effective over clear-sky ocean scenes that are dominated by the Rayleigh scattering radiance. The DCC-based striping correction factors work well at all radiance levels; therefore, they can be deployed operationally to improve the quality of NOAA-20 M1 SDRs. Full article

The Planet SuperDove sensors produce eight-band, three-meter resolution images covering the blue, green, red, red-edge, and NIR spectral bands. Variations in spectral response in the data used to perform atmospheric correction combined with low signal-to-noise over ocean waters can lead to visible striping artifacts in the downstream ocean-color products. It was determined that the striping artifacts could be removed from these products by filtering the top of the atmosphere radiance in the red and NIR bands prior to selecting the aerosol models, without sacrificing high-resolution features in the imagery. This paper examines an approach that applies this filtering to the respective bands as a preprocessing step. The outcome and performance of this filtering technique are examined to assess the success of removing the striping effect in atmospherically corrected Planet SuperDove data. Full article

The optical remote sensors carried by the Jilin-1 KF02 series satellites have an imaging resolution better than 0.5 m and a width of 150 km. There are radiometric problems, such as stripe noise, vignetting, and inter-slice chromatic aberration, in their raw images. In this paper, a relative radiometric correction method based on temperature normalization is proposed for the response characteristics of sensors and the structural characteristics of optical splicing of Jilin-1 KF02 series satellites cameras. Firstly, a model of temperature effect on sensor output is established to correct the variation of sensor response output digital number (DN) caused by temperature variation during imaging process, and the image is normalized to a uniform temperature reference. Then, the horizontal stripe noise of the image is eliminated by using the sensor scan line and dark pixel information, and the vertical stripe noise of the image is eliminated by using the method of on-orbit histogram statistics. Finally, the method of superposition compensation is used to correct the vignetting area at the edge of the image due to the lack of energy information received by the sensor so as to ensure the consistency of the image in color and image quality. The proposed method is verified by Jilin-1 KF02A on-orbit images. Experimental results show that the image response is uniform, the color is consistent, the average Streak Metrics (SM) is better than 0.1%, Root-Mean-Square Deviation of the Mean Line (RA) and Generalized Noise (GN) are better than 2%, Relative Average Spectral Error (RASE) and Relative Average Spectral Error (ERGAS) are greatly improved, which are better than 5% and 13, respectively, and the relative radiation quality is obviously improved after relative radiation correction. Full article

Stripe noise is a general phenomenon in original remote sensing images that both degrades image quality and severely limits its quantitative application. While the classical statistical method is effective in correcting common stripes caused by inaccurately calibrating relative gains and offsets between detectors, it falls short in correcting other nonlinear stripe noises originating from subtle nonlinear changes or random contamination within the same detector. Therefore, this paper proposes a novel trend repair method based on two normal columns directly adjacent to a defective column to rectify the trend by considering the geospatial structure of contaminated pixels, eliminating residual stripe noise evident in level 0 (L0) remote sensing images after histogram matching. GF5-02 VIMI (Gaofen5-02, visual and infrared multispectral imager) images and simulated Landsat 8 thermal infrared sensor (TIRS) images deliberately infused with stripe noise are selected to test the new method and two other existing methods, the piece-wise method and the iterated weighted least squares (WLS) method. The effectiveness of these three methods is reflected by streaking metrics (Streaking), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and improvement factor (IF) on the uniformity, structure, and information content of the corrected GF5-02 VIMI images and by the accuracy of the corrected simulated Landsat 8 TIRS images. The experimental results indicate that the trend repair method proposed in this paper removes nonlinear stripe noise effectively, making the results of IF > 20. The remaining indicators also show satisfactory results; in particular, the mean accuracy derived from the simulated image remains below a digital number (DN) of 15, which is far superior to the other two methods. Full article

The measurement precision of a line-structured light measurement system is directly affected by the accuracy of extracting the center points of the laser stripes. When the measured object’s surface has significant undulations and severe reflections, existing algorithms are prone to issues such as significant susceptibility to noise and the extraction of false center points. To address these issues, an improved unilateral tracing-based structured light centerline extraction algorithm is proposed. The algorithm first performs unilateral and bidirectional tracing on the upper boundary of the preprocessed laser stripes, then uses the grayscale centroid method to extract the initial coordinates of the center points, and finally corrects them by calculating the stripe’s normal direction using the Hessian matrix. Experimental results show that the proposed algorithm can still extract the stripe center points well under strong interference, with the RMSE reduced by 37% compared to the Steger method and the running speed increased by almost 4 times compared to the grayscale centroid method. The algorithm’s strong robustness, high accuracy, and efficiency provide a viable solution for real-time measurement of line-structured light and high-precision three-dimensional reconstruction. Full article

Accurate brain extraction is crucial for the validity of MRI analyses, particularly in the context of traumatic brain injury (TBI), where conventional automated methods frequently fall short. This study investigates the interplay between intensity normalization, bias-field correction (also called intensity inhomogeneity correction), and automated brain extraction in MRIs of individuals with TBI. We analyzed 125 T1-weighted Magnetization-Prepared Rapid Gradient-Echo (T1-MPRAGE) and 72 T2-weighted Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRI sequences from a cohort of 143 patients with moderate to severe TBI. Our study combined 14 different intensity processing procedures, each using a configuration of N3 inhomogeneity correction, Z-score normalization, KDE-based normalization, or WhiteStripe intensity normalization, with 10 different configurations of the Brain Extraction Tool (BET) and the Optimized Brain Extraction Tool (optiBET). Our results demonstrate that optiBET with N3 inhomogeneity correction produces the most accurate brain extractions, specifically with one iteration of N3 for T1-MPRAGE and four iterations for T2-FLAIR, and pipelines incorporating N3 inhomogeneity correction significantly improved the accuracy of BET as well. Conversely, intensity normalization demonstrated a complex relationship with brain extraction, with effects varying by the normalization algorithm and BET parameter configuration combination. This study elucidates the interactions between intensity processing and the accuracy of brain extraction. Understanding these relationships is essential to the effective and efficient preprocessing of TBI MRI data, laying the groundwork for the development of robust preprocessing pipelines optimized for multi-site TBI MRI data. Full article