CCD Images Research Articles

Multisource data fusion for defect detection in composite additive manufacturing using explainable deep neural network

Composite additive manufacturing has the potential to replace traditional composite manufacturing for aerospace applications. Additive manufacturing provides greater adaptability to complex designs, reduce material waste, and streamline the production process. However, in comparison to conventional composite manufacturing methods, additive manufacturing is inherently more susceptible to processing anomalies and defects, primarily due to the novelty of the process and the absence of applied pressure. To effectively capitalize on the benefits of additive manufacturing, a quality and reliability assurance system is critical. In this study, we have generated multisource data to analyze the inner layer thermography and surface quality, employing a multi-camera system including thermal and charge-coupled device cameras. This multisource data is utilized in an explainable zero bias deep neural network framework to detect manufacturing defects. This deep learning algorithm introduces a new zero bias layer following the regular dense layer. After being trained on normal (defect-free) samples of each data stream, the trained model is transformed into an anomaly detector by extracting low dimension features from the zero-bias layer. This allowed identification various anomalies without having to train the model on any defective inputs. As a result, the model's accuracy to detect multiple types of anomalies is higher with multisource data compared to models based on single data source. Anomaly detection accuracy was 99.72% when using data from multiple sources, 98.28% when using only CCD images, and 95% when using only thermal camera data.

Deep Neural Network Closed-loop with Raw Data for Optical Resident Space Object Detection

Optical survey is an important means for observing resident space objects and space situational awareness. With the application of astronomical techniques and reduction method, wide field of view telescopes have made significant contributions in discovering and identifying resident space objects. However, with the development of modern optical and electronic technology, the detection limit of instruments and infrastructure has been greatly extended, leading to an extensive number of raw images and many more sources in these images. Challenges arise when reducing these data in terms of traditional measurement and calibration. Based on the amount of data, it is particularly feasible and reliable to apply machine learning algorithms. Here an end-to-end deep learning framework is developed, it is trained with a priori information on raw detections and the automatic detection task is performed on the new data acquired. The closed-loop is evaluated based on consecutive CCD images obtained with a dedicated space debris survey telescope. It is demonstrated that our framework can achieve high performance compared with the traditional method, and with data fusion, the efficiency of the system can be improved without changing hardware or deploying new devices. The technique deserves a wider application in many fields of observational astronomy.

New astrometric positions for six Jovian irregular satellites using Gaia DR3 in 2016 — 2021

The Jovian system is like a miniature solar system, with the system being more than twice as massive as all the other planets combined and having many natural satellites. Its formation and early evolution had a profound influence on our knowledge of the sculpting of the architecture of the solar system. Astrometric observations are of importance, based on these observations, its orbital dynamics, as well as its origin sometimes, of a solar system object can be determined. We aimed to obtain good astrometric positions of some irregular satellites Elara, Pasiphae, Sinope, Lysithea, Carme and Ananke to refine their orbits and ephemerides, and to understand their dynamics. We have taken, processed and reduced 964 ground-based CCD frames obtained between 2016 and 2021 by the 2.4 m telescope at the Lijiang Station of Yunnan Observatory over 27 nights. Among CCD image processing, the image subtraction technique of ISIS is employed to eliminate star images that are close to or/and almost overlapped with those of our targets in 2019. The star catalog Gaia DR3 is utilized for astrometric calibration, in which a weight scheme is applied to solve more accurate plate model. For all targets, their theoretical positions are retrieved from the Institut de Mécanique Céleste et de Calcul des Éphémérides (IMCCE) ephemeris. Our results show the mean (O - C)s (observed minus computed) of the positional residuals of all targets are −0′′.007 and 0′′.011 in R.A. and Dec., respectively. Their corresponding standard deviations are about 0′′.05 in each direction, except for Ananke (the faintest satellite) with its standard deviation about 0′′.08 in each direction.

Modeling and Calculation of Limit Magnitude Detection of Orbital Optoelectric Tracking System

In order to evaluate the tracking capability of optoelectric tracking for an orbital target, the limit magnitude detection performance calculation model and its calculation method are studied. Combining the optical signal characteristics of the tracked orbital target, the background, and the CCD noise, the framework of the limit magnitude calculation model of the system for dynamic target detection is constructed. The relationships between the limit magnitude and the signal-to-noise ratio threshold of the optical signal characteristics, the exposure time of the CCD camera, and the dark current of the CCD imaging are studied and analyzed while considering the sunlight illumination condition, so that the calculation function and its change curve are given. The limit magnitude detection capability of the system is verified by the simulated experiment and the synchronized tracking test, and the detection distance maximum error of the model calculation is 3.6 m. The results show that under certain illumination conditions, when the exposure time of the CCD camera is longer and the SNR threshold is lower, the limit magnitude detection performance of the system is better, and the tracking performance of the system is more stable.

Superluminal Proper Motion in the X-Ray Jet of Centaurus A

The structure of the jet in Cen A is likely better revealed in X-rays than in the radio band, which is usually used to investigate jet proper motions. In this paper, we analyze Chandra Advanced CCD Imaging Spectrometer observations of Cen A from 2000 to 2022 and develop an algorithm for systematically fitting the proper motions of its X-ray jet knots. Most of the knots had an apparent proper motion below the detection limit. However, one knot at a transverse distance of 520 pc had an apparent superluminal proper motion of 2.7 ± 0.4c. This constrains the inclination of the jet to be i < 41° ± 6° and the velocity of this knot to be β > 0.94 ± 0.02. This agrees well with the inclination measured in the inner jet by the Event Horizon Telescope but contradicts previous estimates based on jet and counterjet brightness. It also disagrees with the proper motion of the corresponding radio knot, of 0.8 ± 0.1c, which further indicates that the X-ray and radio bands trace distinct structures in the jet. There are four prominent X-ray jet knots closer to the nucleus, but only one of these is inconsistent with being stationary. A few jet knots also have a significant proper-motion component in the nonradial direction. This component is typically larger closer to the center of the jet. We also detect brightness and morphology variations at a transverse distance of 100 pc from the nucleus.

High-resolution X-ray spectra of the compact binary supersoft X-ray source CAL 87

In this study we present an analysis of the archival X-ray data of the eclipsing supersoft X-ray binary CAL 87 observed with the Chandra Advanced CCD Imaging Spectrometer (ACIS) camera and Low Energy Transmission Grating (LETG) in 2001 August and with XMM-Newton in 2003 April. The high-resolution X-ray spectra are almost unchanged on the two different dates. The average unabsorbed X-ray luminosity during the exposure was 4.64 − 5.46 × 1036 ergs s−1 in 2001 and 4.54 − 4.82 × 1036 ergs s−1 in 2003, with prominent and redshifted emission lines, mostly of nitrogen, oxygen, iron, and argon, contributing to at least 30% of the X-ray flux. The continuum X-ray flux is at least an order of magnitude too low for a hot hydrogen-burning white dwarf (WD). However, the continuum flux is consistent with Thomson-scattering reflecting about 5% of the light of a hydrogen-burning WD with effective temperature of 800 000 K and a mass of ∼1.2 M⊙. It has been noted that a large Thomson-scattering corona explains the X-ray eclipse of CAL 87, in which the size of the eclipsed region is found to be on the order of a solar radius. The emission lines originate in an even more extended region beyond the eclipsed central X-ray source; the emission spectrum is very complex, with unusual line ratios.

The Chandra Source Catalog Release 2 Series

The Chandra Source Catalog (CSC) is a virtual X-ray astrophysics facility that enables both detailed individual source studies and statistical studies of large samples of X-ray sources detected in Advanced CCD Imaging Spectrometer and High Resolution Camera-I imaging observations obtained by the Chandra X-ray Observatory. The catalog provides carefully curated, high-quality, and uniformly calibrated and analyzed tabulated positional, spatial, photometric, spectral, and temporal source properties, as well as science-ready X-ray data products. The latter includes multiple types of source- and field-based FITS format products that can be used as a basis for further research, significantly simplifying follow-up analysis of scientifically meaningful source samples. We discuss in detail the algorithms used for the CSC Release 2 Series, including CSC 2.0, which includes 317,167 unique X-ray sources on the sky identified in observations released publicly through the end of 2014, and CSC 2.1, which adds Chandra data released through the end of 2021 and expands the catalog to 407,806 sources. Besides adding more recent observations, the CSC Release 2 Series includes multiple algorithmic enhancements that provide significant improvements over earlier releases. The compact source sensitivity limit for most observations is ∼5 photons over most of the field of view, which is ∼2× fainter than Release 1, achieved by coadding observations and using an optimized source detection approach. A Bayesian X-ray aperture photometry code produces robust fluxes even in crowded fields and for low-count sources. The current release, CSC 2.1, is tied to the Gaia-CRF3 astrometric reference frame for the best sky positions for catalog sources.

Deep learning-based denoising of spatial heterodyne spectroscopy interferograms without clear images

A newly developed spectroscopic method called spatial heterodyne spectroscopy (SHS) characterized by grating space diffraction, compact size, high etendue, larger optical tolerance, and no moving parts. Spatial heterodyne spectroscopy has many uses, including atmospheric remote sensing, atmospheric composition research, and so on. However, due to the features of the sensor material and the operating environment, various noise will be introduced throughout the CCD image acquisition process. The imaging performance of the device will be reduced or possibly disabled as a result of these noise effects. We demonstrate how deep learning techniques may be used to denoise SHS images. Furthermore, we propose the use of N2N (noise2noise, noise to noise) method for the difficulty of deep learning to obtain a large number of target-noise pair. This method trains the deep learning network using only noisy images, which greatly reduces the difficulty of acquiring training data. We analyzed denoising data for monochromatic and continuous light. In terms of interferograms, the experimental results show that the deep neural network trained using this method can effectively recover interferogram image information, suppress noise, and maintain the continuity of the interference fringe edge. In terms of spectrograms, the deep neural network trained by this approach can suppress noise in monochromatic spectra and retrieve spectral signals damaged by noise in continuous spectra. As a result, the technique is suitable for spatial heterodyne spectroscopy denoising.

Subaru Suprime-Cam Wide-field BVI Stellar Photometry of the M33 Galaxy

We have surveyed the complete extent of the disk of M33—a gas-rich low-mass dwarf spiral galaxy in the Local Group. The B-, V-, and I-passband (the Johnson–Cousins system) CCD images (typical seeing ∼0.″8) were obtained with the Subaru Telescope equipped with the Suprime-Cam mosaic camera. The wide-field (∼1.°0 × 1.°5) catalog of 803,095 (15 ≤ V ≤ 25) starlike objects, measured using the point-spread function and aperture photometry techniques, is presented. We determined the distance modulus of M33 using the tip of the red giant branch (I TRGB = 20.64 ± 0.02) as a reference point of (m − M)0 = 24.63 ± 0.02stat ± 0.06syst (843 kpc). We found young (≲100 Myr) stellar populations residing up to the deprojected radius of ∼10 kpc. The scale length of the young main-sequence (MS) star surface-number density in the range of radial distances from 7 to 9 kpc is 0.53 ± 0.03 kpc. The youngest MS stars (≲15 Myr) reside up to the radius of ∼8 kpc. This distribution of stellar populations may suggest an outside-in scenario of recent star formation in the disk of M33.

Nanoradians level high resolution autocollimation method based on array slits.

This study developed a detailed mathematical model to elucidate array slits imaging and linear CCD acquisition. Frequency domain analysis revealed the relationship between system parameters and spot image aliasing noise. Aliasing noise was reduced through optimized array slits design and linear CCD parameter selection, improving angular measurement resolution and stability. Unlike in traditional methods, only 8 array slits were required to achieve nanoradian-level resolution and stability. The device achieved an angular measurement resolution of 0.0005 arcsec over a measurement range of ±400 arcsec. Static repeatability is 0.0003 arcsec, and stability is 0.0061 arcsec in 2 hours.

Single-pixel Fresnel incoherent correlation holography compressed imaging using a Trumpet network

Fresnel incoherent correlation holography (FINCH) can achieve high-precision and non-scanning 3D imaging. However, as a holographic imaging technology, the huge bandwidth requirements and the amount of holographic data transmitted have always been one of the important factors limiting its application. In addition, the hardware cost of pixel array-based CCD or CMOS imaging is very high under high resolution or specific wavelength conditions. Accordingly, a single-pixel Fresnel incoherent correlation holography (SP-FINCH) compressed imaging method is proposed, which replaces pixel array detector with single-pixel detector and designs a Trumpet network to achieve low-cost and high-resolution imaging. Firstly, a modified FINCH imaging system is constructed and data acquisition is carried out using a single-pixel detector. Secondly, a Trumpet network is constructed to directly map the relationship between one-dimensional sampled data and two-dimensional image in an end-to-end manner. Moreover, by comparing the reconstructed images using neural network with that using commonly used single-pixel reconstruction methods, the results indicate that the proposed SP-FINCH compressed imaging method can significantly improve the quality of image reconstruction at lower sampling rate and achieve imaging without phase-shifting operation. The proposed method has been shown to be feasible and advantageous through numerical simulations and optical experiment results.

Soft X-ray wavefront sensing at an ellipsoidal mirror shell

A reliable `in situ' method for wavefront sensing in the soft X-ray domain is reported, developed for the characterization of rotationally symmetric optical elements, like an ellipsoidal mirror shell. In a laboratory setup, the mirror sample is irradiated by an electron-excited (4.4 keV), micrometre-sized (∼2 µm) fluorescence source (carbon K α, 277 eV). Substantially, the three-dimensional intensity distribution I(r) is recorded by a CCD camera (2048 × 512 pixels of 13.5 µm) at two positions along the optical axis, symmetrically displaced by ±21–25% from the focus. The transport-of-intensity equation is interpreted in a geometrical sense from plane to plane and implemented as a ray tracing code, to retrieve the phase Φ(r) from the radial intensity gradient on a sub-pixel scale. For reasons of statistical reliability, five intra-/extra-focal CCD image pairs are evaluated and averaged to an annular two-dimensional map of the wavefront error {\cal W}. In units of the test wavelength (C K α), an r.m.s. value \sigma_{\cal{W}} = ±10.9λ0 and a peak-to-valley amplitude of ±31.3λ0 are obtained. By means of the wavefront, the focus is first reconstructed with a result for its diameter of 38.4 µm, close to the direct experimental observation of 39.4 µm (FWHM). Secondly, figure and slope errors of the ellipsoid are characterized with an average of ±1.14 µm and ±8.8 arcsec (r.m.s.), respectively, the latter in reasonable agreement with the measured focal intensity distribution. The findings enable, amongst others, the precise alignment of axisymmetric X-ray mirrors or the design of a wavefront corrector for high-resolution X-ray science.

Revisiting the abundance pattern and charge-exchange emission in the centre of M 82

Context. The interstellar medium (ISM) in starburst galaxies contains many chemical elements that are synthesised by core-collapse supernova explosions. By measuring the abundances of these metals, we can study the chemical enrichment within the galaxies and the transportation of metals into the circumgalactic environment through powerful outflows. Aims. We performed a spectral analysis of the X-ray emissions from the core of M 82 using the Reflection Grating Spectrometer (RGS) on board XMM-Newton to accurately estimate the metal abundances in the ISM. Methods. We analysed over 300 ks of RGS data observed with 14 position angles, covering a cross-dispersion width of 80 arcsec. We employed multi-temperature thermal plasma components in collisional ionisation equilibrium (CIE) to reproduce the observed spectra, each of which exhibited a different spatial broadening. Results. The O VII band CCD image shows a broader distribution that those for the O VIII and Fe-L bands. The O VIII line profiles have a prominent double-peaked structure that corresponds to the north- and southward outflows. The O VII triplet feature exhibits marginal peaks. A single CIE component that is convolved with the O VII band image approximately reproduces the spectral shape. A CIE model combined with a charge-exchange emission model also successfully reproduces the O VII line profiles. However, the ratio of these two components varies significantly with the observed position angles, which is physically implausible. Spectral fitting of the broadband spectra suggests a multi-temperature phase in the ISM that is approximated by three components at 0.1, 0.4, and 0.7 keV. Notably, the 0.1 keV component exhibits a broader distribution than the 0.4 and 0.7 keV plasmas. The derived abundance pattern shows super-solar N/O, solar Ne/O and Mg/O, and half-solar Fe/O ratios. These results indicate the chemical enrichment by core-collapse supernovae in starburst galaxies.

SAUNAS. I. Searching for Low Surface Brightness X-Ray Emission with Chandra/ACIS

We present Selective Amplification of Ultra Noisy Astronomical Signal (SAUNAS), a pipeline designed for detecting diffuse X-ray emission in the data obtained with the Advanced CCD Imaging Spectrometer (ACIS) of the Chandra X-ray Observatory. SAUNAS queries the available observations in the Chandra archive and performs photometric calibration, point-spread function modeling and deconvolution, point-source removal, adaptive smoothing, and background correction. This pipeline builds on existing and well-tested software including CIAO, VorBin, and LIRA. We characterize the performance of SAUNAS through several quality performance tests and demonstrate the broad applications and capabilities of SAUNAS using two galaxies already known to show X-ray-emitting structures. SAUNAS successfully detects the 30 kpc X-ray superwind of NGC 3079 using Chandra/ACIS data sets, matching the spatial distribution detected with more sensitive XMM-Newton observations. The analysis performed by SAUNAS reveals an extended low surface brightness source in the field of UGC 5101 in the 0.3–1.0 keV and 1.0–2.0 keV bands. This source is potentially a background galaxy cluster or a hot gas plume associated with UGC 5101. SAUNAS demonstrates its ability to recover previously undetected structures in archival data, expanding exploration into the low surface brightness X-ray Universe with Chandra/ACIS.

Multiwavelength photometric study of RR lyrae variables in the globular cluster NGC 5272 (Messier 3)

ABSTRACT We present a comprehensive photometric study of RR Lyrae stars in the M3 globular cluster, utilizing a vast data set of 3140 optical (UBVRI) CCD images spanning 35 yr from astronomical data archives. We have successfully identified previously known 238 RR Lyrae stars from the photometric data, comprising 178 RRab, 49 RRc, and 11 RRd stars. Multiband periodogram was used to significantly improve the long-term periods of 65 per cent of RR Lyrae stars in our sample, thanks to the unprecedentedly long temporal coverage of the observations. The light curve templates were used to obtain accurate and precise mean magnitudes and amplitudes of all RR Lyrae variables. We combined optical (UBVRI) and near-infrared (NIR, JHKs) photometry of RR Lyrae variables to investigate their location in the colour-magnitude diagrams as well as the pulsation properties such as period distributions, Bailey diagrams and amplitude ratios. The period–luminosity relations in R and I bands and Period–Wesenheit relations were derived after excluding outliers identified in CMDs. The Period–Wesenheit relations calibrated via the theoretically predicted relations were used to determine a distance modulus of $\mu = 15.04 \pm 0.04 \, {\rm (stats)} \pm 0.19 \, {\rm {(syst.)}}$ mag (using metal-independent WBV Wesenheit) and $\mu = 15.03 \pm 0.04 \, {\rm (stats)} \pm 0.17 \, {\rm {(syst.)}}$ mag (using metal-dependent WVI Wesenheit). Our distance measurements are in excellent agreement with published distances to M3 in the literature. We also employed an artificial neural network based comparison of theoretical and observed light curves to determine physical parameters (mass, luminosity, and effective temperature) for 79 non-Blazhko RRab stars that agree with limited literature measurements.

Comet 81P/Wild 2: Changes in the spin axis orientation during the last five apparitions

Comet 81P/Wild 2 is characterized by the presence of a prominent fan-shaped dust emission originating from an active source at high latitude on the nucleus, whose axis is assumed to coincide with the comet's rotation axis. Therefore, several authors estimated the spin axis orientation of 81P in past apparitions based on the polar jet model.By measuring the PAs of the fan on CCD images taken with different telescopes during the 2009–10 and 2022–23 apparitions, we estimated a position of the comet's spin axis at RA = 295.0° ± 7.5°, Dec = 14.5° ± 4.0° for the 2009–10 apparition and at RA = 296.7 ± 2.0°, Dec = 17.3 ± 2.5° for the 2022–23 apparition. Despite some degree of uncertainty of the estimate for the 2009–10 apparition, we interpolated the estimate for 2009–10 and 2022–23 with the published data of the previous apparition of 1997, to assess the presence and the extent of a drift of the pole since the 1997 passage.The analysis over a long time span of five consecutive apparitions confirms previous observations that the spin axis of comet 81P is subject to a slow drift with variable rate, probably connected to outgassing-induced jet forces and the related non-gravitational perturbations of its orbital period.

X-ray spectral properties of dust-obscured galaxies in the XMM-SERVS coverage of the XMM-LSS field

ABSTRACT With an aim to unveil the population of obscured active galactic nuclei (AGN) hosted in high-z dust-obscured galaxies (DOGs), we performed X-ray spectral study of 34 DOGs (0.59 ≤ z ≤ 4.65) lying within 5.3 square degrees of the XMM-Spitzer Extragalactic Representative Volume Survey (XMM-SERVS) coverage in the XMM-Large Scale Structure (XMM-LSS) field. To improve the spectral quality of individual sources, we combined all the existing XMM−Newton data and also included Chandra/Advanced CCD Imaging Spectrometer (ACIS) data, whenever available. We find that the X-ray spectra of our DOGs can be fitted with a simple absorbed power law or with a physically motivated borus02 model. The line-of-sight column densities (NH) in our sources span across a wide range (1.02 × 1022 cm−2 ≤ NH ≤ 1.21 × 1024 cm−2), with a substantial fraction (∼17.6 per cent) of them being heavily obscured (NH ≥ 1023 cm−2). We also identified one new Compton-thick (CT)-AGN candidate, yielding the CT-AGN fraction in our sample to be only 3 per cent. The absorption-corrected 2.0–10 keV X-ray luminosities of our sources (2.00 × 1043 erg s−1 ≤ $L_{\rm 2-10~keV}^{\rm int}$ ≤ 6.17 × 1045 erg s−1) suggest them to be luminous quasars. The NH versus Eddington ratio diagnostic plot infers that our sample consists of a heterogeneous population that includes a small fraction (∼12 per cent) of DOGs belonging to an early phase (Hot DOGs) during which accretion and obscuration peaks, while the remaining DOGs belong to an intermediate or late phase during which radiative feedback from the dominant AGN blows away surrounding obscuring material.

Precise surface machining of fused silica using high stability atmospheric pressure microwave plasma jet with a new internal electrode

Plasma is a promising approach for machining of fused glass substrates. Developing high-performance microwave torch is the core element to ensure the processing accuracy of this technology. In this study, a spline curve-based microwave torch internal electrode structure was proposed, which increased the internal electric field strength by 54 %. The discharge dynamics simulation based on this electrode shows that the plasma is excited at the inner electrode tip and the bottom of the resonant cavity, then it expands to form a linear discharge region and eventually fill the discharge tube. It was revealed by CCD images that with the increase of carrier gas flow rate, turbulent flow state appears in the tail of the jet. OES data shows that microwave Ar plasma can effectively dissociate CF4, and due to the oxidation effect of oxygen, CFx and C2 content decrease significantly with the addition of oxygen. The machining experiment on fused silica shows that the material removal stability of this torch reaches 3.69 %, and the removal rate increases nYarly with dwell time due to thermal effect. Finally, the machining performance of the atmospheric pressure microwave plasma jet was verified by figuring of a planar fused silica surface reducing the form error from 108.1 nm RMS to 16.5 nm RMS.

Long-term monitoring chlorophyll-a concentration using HJ-1 A/B imagery and machine learning algorithms in typical lakes, a cold semi-arid region.

Chlorophyll a (Chl-a) in lakes serves as an effective marker for assessing algal biomass and the nutritional level of lakes, and its observation is feasible through remote sensing methods. HJ-1 (Huanjing-1) satellite, deployed in 2008, incorporates a CCD capable of a 30 m resolution and has a revisit interval of 2 days, rendering it a superb choice or supplemental sensor for monitoring trophic state of lakes. For effective long-term and regional-scale mapping, both the imagery and the evaluation of machine learning algorithms are essential. The several typical machine learning algorithms, i.e., Support Vector Regression (SVR), Gradient Boosting Decision Trees (GBDT), XGBoost (XGB), Random Forest (RF), K-Nearest Neighbor (KNN), Kernel Ridge Regression (KRR), and Multi-Layer Perception Network (MLP), were developed using our in-situ measured Chl-a. A cross-validation grid to identify the most effective hyperparameter combinations for each algorithm was used, as well as the selected optimal superparameter combinations. In Chl-a mapping of three typical lakes, the R2 of GBDT, XGB, RF, and KRR all reached 0.90, while XGB algorithm also exhibited stable performance with the smallest error (RMSE = 3.11 μg/L). Adjustments were made to align the Chl-a spatial-temporal patterns with past data, utilizing HJ1-A/B CCD images mapping through XGB algorithm, which demonstrates its stability. Our results highlight the considerable effectiveness and utility of HJ-1 A/B CCD imagery for evaluation and monitoring trophic state of lakes in a cold arid region, providing the application cases contribute to the ongoing efforts to monitor water qualities.

An Optimized Smoke Segmentation Method for Forest and Grassland Fire Based on the UNet Framework

Smoke, a byproduct of forest and grassland combustion, holds the key to precise and rapid identification—an essential breakthrough in early wildfire detection, critical for forest and grassland fire monitoring and early warning. To address the scarcity of middle–high-resolution satellite datasets for forest and grassland fire smoke, and the associated challenges in identifying smoke, the CAF_SmokeSEG dataset was constructed for smoke segmentation. The dataset was created based on GF-6 WFV smoke images of forest and grassland fire globally from 2019 to 2022. Then, an optimized segmentation algorithm, GFUNet, was proposed based on the UNet framework. Through comprehensive analysis, including method comparison, module ablation, band combination, and data transferability experiments, this study revealed that GF-6 WFV data effectively represent information related to forest and grassland fire smoke. The CAF_SmokeSEG dataset was found to be valuable for pixel-level smoke segmentation tasks. GFUNet exhibited robust smoke feature learning capability and segmentation stability. It demonstrated clear smoke area delineation, significantly outperforming UNet and other optimized methods, with an F1-Score and Jaccard coefficient of 85.50% and 75.76%, respectively. Additionally, augmenting the common spectral bands with additional bands improved the smoke segmentation accuracy, particularly shorter-wavelength bands like the coastal blue band, outperforming longer-wavelength bands such as the red-edge band. GFUNet was trained on the combination of red, green, blue, and NIR bands from common multispectral sensors. The method showed promising transferability and enabled the segmentation of smoke areas in GF-1 WFV and HJ-2A/B CCD images with comparable spatial resolution and similar bands. The integration of high spatiotemporal multispectral data like GF-6 WFV with the advanced information extraction capabilities of deep learning algorithms effectively meets the practical needs for pixel-level identification of smoke areas in forest and grassland fire scenarios. It shows promise in improving and optimizing existing forest and grassland fire monitoring systems, providing valuable decision-making support for fire monitoring and early warning systems.