Search Results (85)

Ghost imaging (GI) offers a robust framework for remote sensing under degraded visibility conditions. However, atmospheric scattering in phenomena such as fog introduces significant noise and signal attenuation, thereby limiting its efficacy. Inspired by the selective attention mechanisms of biological visual systems, this study introduces a novel deep learning (DL) architecture that embeds a self-attention mechanism to enhance GI reconstruction in foggy environments. The proposed approach mimics neural processes by modeling both local and global dependencies within one-dimensional bucket measurements, enabling superior recovery of image details and structural coherence even at reduced sampling rates. Extensive simulations on the Modified National Institute of Standards and Technology (MNIST) and a custom Human-Horse dataset demonstrate that our bio-inspired model outperforms conventional GI and convolutional neural network-based methods. Specifically, it achieves Peak Signal-to-Noise Ratio (PSNR) values between 24.5–25.5$\mathrm{dB}/\mathrm{m}$ and Structural Similarity Index Measure (SSIM) values of approximately 0.8 under high scattering conditions ($\beta \ge 3.0 \mathrm{dB}/\mathrm{m}$) and moderate sampling ratios ($N \ge 50\%$). A comparative analysis confirms the critical role of the self-attention module, providing high-quality image reconstruction over baseline techniques. The model also maintains computational efficiency, with inference times under $0.12 \mathrm{s}$, supporting real-time applications. This work establishes a new benchmark for bio-inspired computational imaging, with significant potential for environmental monitoring, autonomous navigation and defense systems operating in adverse weather. Full article

Impaired visibility, a major global environmental threat, is a result of light scattering by atmospheric particulate matter. While digital photographs are increasingly used for daytime visibility estimation, such methods are largely ineffective at night owing to the different scattering effects. Here, we introduce an image-based algorithm for inferring nighttime visibility from a single photograph by analyzing the forward scattering index and optical thickness retrieved from glow effects around light sources. Using photographs crawled from social media platforms across mainland China, we estimated the nationwide visibility for one year using the proposed algorithm, achieving high goodness-of-fit values (R² = 0.757; RMSE = 4.318 km), demonstrating robust performance under various nighttime scenarios. The model also captures both chronic and episodic visibility degradation, including localized pollution events. These results highlight the potential of using ubiquitous smartphone photography as a low-cost, scalable, and real-time sensing solution for nighttime atmospheric monitoring in urban areas. Full article

Canopy chlorophyll content (CCC) is a key indicator for assessing the carbon sequestration capacity and material cycling efficiency of ecosystems, and its accurate retrieval holds significant importance for analyzing ecosystem functioning. Although numerous destructive and remote sensing methods have been developed to estimate CCC, the accurate estimation of CCC remains a significant challenge in mountainous regions with complex terrain and heterogeneous vegetation types. Through the synergistic analysis of ground hyperspectral and Sentinel-2 data, this study employed Pearson correlation analysis and spectral resampling techniques to identify Sentinel-2 blue band B1 (443 nm) and red band B4 (665 nm) as chlorophyll-sensitive bands through spectral matching with the hyperspectral reflectance of typical grassland vegetation. Based on this, we developed a new four-band vegetation index (VI), the Dual Red-edge and Coastal Aerosol Vegetation Index (DRECAVI), for estimating the CCC of heterogeneous grasslands in the middle section of the Tianshan Mountains. DRECAVI incorporates red-edge anti-saturation modules (bands B4 and B7) and aerosol correction modules (bands B1 and B8). In order to test the performance of the new index, we compared it with eight commonly used indices and a hybrid model, the Sentinel-2 Biophysical Processor (S2BP). The results indicated the following: (1) DRECAVI demonstrated the highest accuracy in CCC retrieval for mountainous vegetation (R² = 0.74, RMSE = 16.79, MAE = 12.50) compared to other VIs and hybrid methods, effectively mitigating saturation effects in high biomass areas and capturing a weak bimodal distribution pattern of CCC in the montane meadow. (2) The blue band B1 enhances atmospheric correction robustness by suppressing aerosol scattering, and the red-edge band B7 overcomes the sensitivity limitations of conventional red-edge indices (such as NDVI705, CIred-edge, and NDRE), demonstrating the potential application of the synergy mechanism between the blue band and the red-edge band. (3) Although the S2BP achieved high accuracy (R² = 0.73, RMSE = 19.83, MAE = 14.71) without saturation effects and detected a bimodal distribution of CCC in the montane meadow of the study area, its algorithmic complexity hindered large-scale operational applications. In contrast, DRECAVI maintained similar precision while reducing algorithmic complexity, making it more suitable for regional-scale grassland dynamic monitoring. This study confirms that the synergistic use of multi-source data effectively overcomes the limitations of the spectral–spatial resolution of a single data source, providing a novel methodology for the precision monitoring of mountain ecosystems. Full article

Aerosol optical properties and radiative forcing critically influence Earth’s climate, particularly in semi-arid regions. This study investigates these properties in Yinchuan, Northwest China, focusing on aerosol optical depth (AOD), single-scattering albedo (SSA), Ångström Index, and direct radiative forcing (DRF) using 2023 CE-318 sun photometer data, HYSPLIT trajectory analysis, and the SBDART model. Spring AOD peaks at 0.58 ± 0.15 (500 nm) due to desert dust, with coarse-mode particles dominating, while summer SSA reaches 0.94, driven by fine-mode aerosols. Internal mixing of dust and anthropogenic aerosols significantly alters DRF through enhanced absorption, with spring surface DRF at −101 ± 22W m⁻² indicating strong cooling and internal mixing increasing atmospheric DRF to 52.25W m⁻². These findings elucidate dust–anthropogenic interactions’ impact on optical properties and radiative forcing, offering critical observations for semi-arid climate research. Full article

For the clearing of hazy images, it is difficult to obtain dehazing datasets with paired mapping images. Currently, most algorithms are trained on synthetic datasets with insufficient complexity, which leads to model overfitting. At the same time, the physical characteristics of fog in the real world are ignored in most current algorithms; that is, the degree of fog is related to the depth of field and scattering coefficient. Moreover, most current dehazing algorithms only consider the image dehazing of land scenes and ignore maritime scenes. To address these problems, we propose a multi-scene image dehazing algorithm based on an improved cycle generative adversarial network (CycleGAN). The generator structure is improved based on the CycleGAN model, and a feature fusion attention module is proposed. This module obtains relevant contextual information by extracting different levels of features. The obtained feature information is fused using the idea of residual connections. An attention mechanism is introduced in this module to retain more feature information by assigning different weights. During the training process, the atmospheric scattering model is established to guide the learning of the neural network using its prior information. The experimental results show that, compared with the baseline model, the peak signal-to-noise ratio (PSNR) increases by 32.10%, the structural similarity index (SSIM) increases by 31.07%, the information entropy (IE) increases by 4.79%, and the NIQE index is reduced by 20.1% in quantitative comparison. Meanwhile, it demonstrates better visual effects than other advanced algorithms in qualitative comparisons on synthetic datasets and real datasets. Full article

The application value of low-quality photos taken in foggy conditions is significantly lower than that of clear images. As a result, restoring the original image information and enhancing the quality of damaged images on cloudy days are crucial. Commonly used deep learning techniques like DehazeNet, AOD-Net, and Li have shown encouraging progress in the study of image dehazing applications. However, these methods suffer from a shallow network structure leading to limited network estimation capability, reliance on atmospheric scattering models to generate the final results that are prone to error accumulation, as well as unstable training and slow convergence. Aiming at these problems, this paper proposes an improved end-to-end convolutional neural network method based on the denoising self-encoder-DenseNet (DAE-DenseNet), where the denoising self-encoder is used as the main body of the network structure, the encoder extracts the features of haze images, the decoder performs the feature reconstruction to recover the image, and the boosting module further performs the feature fusion locally and globally, and finally outputs the dehazed image. Testing the defogging effect in the public dataset, the PSNR index of DAE-DenseNet is 22.60, which is much higher than other methods. Experiments have proved that the dehazing method designed in this paper is better than other algorithms to a certain extent, and there is no color oversaturation or an excessive dehazing phenomenon in the image after dehazing. The dehazing results are the closest to the real image and the viewing experience feels natural and comfortable, with the image dehazing effect being very competitive. Full article

Drone vision is widely used in change detection, disaster response, and military reconnaissance due to its wide field of view and flexibility. However, under haze and thin cloud conditions, image quality is usually degraded due to atmospheric scattering. This results in issues like color distortion, reduced contrast, and lower clarity, which negatively impact the performance of subsequent advanced visual tasks. To improve the quality of unmanned aerial vehicle (UAV) images, we propose a dehazing method based on calibration of the atmospheric scattering model. We designed two specialized neural network structures to estimate the two unknown parameters in the atmospheric scattering model: the atmospheric light intensity A and medium transmission t. However, calculation errors always occur in both processes for estimating the two unknown parameters. The error accumulation for atmospheric light and medium transmission will cause the deviation in color fidelity and brightness. Therefore, we designed an encoder-decoder structure for irradiance guidance, which not only eliminates error accumulation but also enhances the detail in the restored image, achieving higher-quality dehazing results. Quantitative and qualitative evaluations indicate that our dehazing method outperforms existing techniques, effectively eliminating haze from drone images and significantly enhancing image clarity and quality in hazy conditions. Specifically, the compared experiment on the R100 dataset demonstrates that the proposed method improved the peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM) metrics by 6.9 dB and 0.08 over the second-best method, respectively. On the N100 dataset, the method improved the PSNR and SSIM metrics by 8.7 dB and 0.05 over the second-best method, respectively. Full article

Haze imagery suffers from reduced clarity, which can be attributed to atmospheric conditions such as dust or water vapor, resulting in blurred visuals and heightened brightness due to light scattering. Conventional methods employing the dark channel prior (DCP) for transmission map estimation often excessively amplify fogged sky regions, causing image distortion. This paper presents a novel approach to improve transmission map granularity by utilizing multiple $1\times 1$ DCPs derived from multiscale hazy, inverted, and Euclidean difference images. An adaptive airlight estimation technique is proposed to handle low-light, hazy images. Furthermore, an adaptive gamma correction method is introduced to refine the transmission map further. Evaluation of dehazed images using the Dehazing Quality Index showcases superior performance compared to existing techniques, highlighting the efficacy of the enhanced transmission map. Full article

Cirrus clouds have an extensive global coverage and their high altitude means they play a critical role in the atmospheric radiation balance. Hexagonal ice plates and columns are two of the most abundant species present in cirrus and yet there remains a poor understanding of how surface roughness affects the scattering of light from these particles. To advance current understanding, the scattering properties of hexagonal ice plates with varying surface roughness properties are simulated using the discrete dipole approximation and the parent beam tracer physical–optics method. The ice plates are chosen to have a volume-equivalent size parameter of $2\pi r/\lambda =60$, where r is the radius of the volume-equivalent sphere, and a refractive index $n=1.31+0i$ at a wavelength $\lambda =0.532$ µm. The surface roughness is varied in terms of a characteristic length scale and an amplitude. The particles are rotated into 96 orientations to obtain the quasi-orientation averaged scattering Mueller matrix and integrated single-scattering parameters. The study finds that the scattering is largely invariant with respect to the roughness length scale, meaning it can be characterised solely by the roughness amplitude. Increasing the amplitude is found to lead to a decrease in the asymmetry parameter. It is also shown that roughness with an amplitude much smaller than the wavelength has almost no effect on the scattering when compared with a pristine smooth plate. The parent beam tracer method shows good agreement with the discrete dipole approximation when the characteristic length scale of the roughness is several times larger than the wavelength, with a computation time reduced by a factor of approximately 500. Full article

In free-space optical communication, the transmission of signal light and beacon light of differing wavelengths through the same atmospheric channel encounters variations in how the atmospheric refractive index absorbs and scatters light. This leads to distinct degrees of wavefront aberrations between the signal and beacon lights. In this study, we employed statistical optics to derive wavefront phase structure functions for both signal and beacon lights under conditions of strong turbulence. We explored how wavefront distortion varies among beams of different wavelengths after propagation through such turbulent conditions. Our findings revealed that as the turbulence outer scale escalates, the difference in wavefront distortion between signal and beacon lights stabilizes after an initial increase, assuming constant wavelengths. Furthermore, we observed significant changes in the relative wavefront aberrations when the inner scale of turbulence surpasses the separation between two points on the receiving apertures. As the disparity in wavelength decreases, so does the difference in wavefront aberrations. Finally, we propose a method for correcting wavefront aberrations based on coefficients of Zernike polynomials corresponding to beams with different wavelengths. This approach is validated through simulation and experimentation, demonstrating an 11% enhancement in the signal-to-optical Strehl ratio and a 0.072 increase in spot energy after the addition of correction coefficients compared with before their inclusion. These results solidify the efficacy of our method in improving adaptive optics correction accuracy. Full article

Applying standard aerosol models for atmospheric correction in nearshore coastal waters introduces significant uncertainties due to their inability to accurately represent aerosol characteristics in these regions. To improve the accuracy of remote sensing reflectance ($R_{rs}$) products in the nearshore waters of the Shandong Peninsula, this study develops an aerosol model based on aerosol data collected from the Mu Ping site in the coastal area of the Shandong Peninsula, enabling tailored atmospheric correction for this specific region. Given the pronounced seasonal variations in aerosol optical properties, monthly aerosol models were developed. The monthly aerosol model is derived using the average values of aerosol microphysical properties. Compared to the standard aerosol model, this model is more effective in characterizing the absorption and scattering characteristics of aerosols in the study area. Corresponding lookup tables for the aerosol model were created and integrated into the NIR-SWIR atmospheric correction algorithm. According to the accuracy evaluation indexes of RMSD, MAE, and UPD, it can be found that the atmospheric correction results of the aerosol model established in this paper are better than those of the standard aerosol model, especially in the 547 nm band. It demonstrates that the new aerosol model outperforms the standard model in atmospheric correction performance. With the increasing availability of aerosol observational data, the aerosol model is expected to become more accurate and applicable to other satellite missions. Full article

A rare event of mixed biomass-burning and polluted dust aerosols was observed over Athens, Greece (37.9° N, 23.6° E), during 21–26 May 2014. This event was studied using a synergy of a 6-wavelength elastic-Raman-depolarization lidar measurements, a CIMEL sun photometer, and in situ instrumentation. The FLEXPART dispersion model was used to identify the aerosol sources and quantify the contribution of dust and black carbon particles to the mass concentration. The identified air masses were found to originate from Kazakhstan and Saharan deserts, under a rare atmospheric pressure system. The lidar ratio (LR) values retrieved from the Raman lidar ranged within 25–89 sr (355 nm) and 35–70 sr (532 nm). The particle linear depolarization ratio (δ_aer) ranged from 7 to 28% (532 nm), indicating mixing of dust with biomass-burning particles. The aerosol optical depth (AOD) values derived from the lidar ranged from 0.09–0.43 (355 nm) to 0.07–0.25 (532 nm). An inversion algorithm was used to derive the mean aerosol microphysical properties (mean effective radius (r_eff), single scattering albedo (SSA), and mean complex refractive index (m)) inside selected atmospheric layers. We found that r_eff was 0.12–0.51 (±0.04) µm, SSA was 0.94–0.98 (±0.19) (at 532 nm), while m ranged between 1.39 (±0.05) + 0.002 (±0.001)i and 1.63 (±0.05) + 0.008 (±0.004)i. The polarization lidar photometer networking (POLIPHON) algorithm was used to estimate the vertical profile of the mass concentration for the dust and non-dust components. A mean mass concentration of 15 ± 5 μg m⁻³ and 80 ± 29 μg m⁻³ for smoke and dust was estimated for selected days, respectively. Finally, the retrieved aerosol microphysical properties were compared with column-integrated sun photometer CIMEL data with good agreement. Full article

The Ganges–Brahmaputra estuary, located in the northern Bay of Bengal, is situated within the largest delta in the world. This river basin features a complex river system, a dense population, and significant variation in watershed vegetation cover. Human activities have significantly impacted the concentration of total suspended matter (TSM) in the estuary and the ecological environment of the adjacent bay. In this study, we utilised the Landsat series of satellite remote sensing data from 1990 to 2020 for TSM retrieval. We applied an atmospheric correction algorithm based on the general purpose exact Rayleigh scattering look-up-table (LUT) and the shortwave-infrared (SWIR) bands extrapolation to Landsat L1 products to obtain high-precision remote sensing reflectance. In conjunction with the normalised difference vegetation index (NDVI), precipitation, and discharge data, we analysed the variation and influencing mechanisms of TSM in the Ganges–Brahmaputra estuary and its surrounding areas. We revealed notable seasonal variation in TSM in the estuary, with higher concentrations during the wet season (May–October) compared to the dry season (the rest of the year). Over the period from 1990 to 2020, the NDVI in the watershed exhibited a significant upward trend. The outer estuarine regions of the Hooghly River and Meghna River displayed significant decreases in TSM, whereas the Baleswar River, which flows through mangrove areas, showed no significant trend in TSM. The declining trend in TSM was mainly attributed to land-use changes and anthropogenic activities, including the construction of embankments, dams, and mangrove conservation efforts, rather than to runoff and precipitation. Surface sediment concentration and chlorophyll in the northern Bay of Bengal exhibited slight increases, which means the limited influence of terrestrial inputs on long-term change in surface sediment concentration and chlorophyll in the northern Bay of Bengal. This study emphasises the impact of human activities on the river–estuary–coast continuum and sheds light on future sustainable management. Full article

In order to investigate the equivalent complex refractive index of atmospheric aerosols near the Earth’s surface, we conducted measurements in the Hefei region from March to April 2022. These measurements utilized a micro-pulse lidar, an Aethalometer, and a Portable Optical Particle Profiler. These measurements encompassed aerosol particle size distribution as well as standard meteorological parameters including temperature, humidity, atmospheric pressure, and wind speed. Subsequently, this dataset was employed to develop an optimization algorithm for retrieving the equivalent complex refractive indices of near-surface aerosols. The methodology relies on lookup tables containing data for extinction efficiency and absorption efficiency factors. It operates on the premise of aerosol property stability within a defined time frame, utilizing measured extinction and absorption coefficients as simultaneous constraints during this period to inversely derive both the real and imaginary parts of the aerosol complex refractive index. Results from the simulation analysis reveal that the newly optimized retrieval algorithm, which relies on lookup tables, exhibits reduced sensitivity to instrument errors when compared to single-point constraint algorithms. This enhancement results in a more efficient and dependable approach for retrieving the aerosol complex refractive index. Empirical inversion and simulation studies were carried out to determine the aerosol equivalent complex refractive index in the Hefei region, utilizing measured data. This inversion process yielded an average complex refractive index of 1.48-i0.017 for aerosols in the Hefei region throughout the experimental period. Correlation analysis unveiled a positive association between the real part of the aerosol complex refractive index and the single-scattering albedo (SSA), whereas the imaginary part displayed a linear negative correlation with the SSA. The mathematical relationship between the real part and the SSA is $y=0.19x+0.62$, and the corresponding relationship between the imaginary part and the SSA is $y=-5.3x+0.99$. This research offers a novel method for the retrieval of the aerosol equivalent complex refractive index. Full article

The advantages of performing aerosol retrieval with multi-angle, multi-spectral photopolarimetric measurements over intensity-only measurements come from this technique’s sensitivity to aerosols’ microphysical properties, such as their particle size, shape, and complex refraction index. In this study, an extended LUT (Look Up Table) algorithm inherited from a previous work based on the assumption of surface reflectance spectral shape invariance is proposed and applied to PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) measurements to retrieve aerosols’ optical properties including aerosol optical depth (AOD) and aerosol fine-mode fraction (FMF). Case studies conducted over East China for different aerosol scenes are investigated. A comparison between the retrieved AOD regional distribution and the corresponding MODIS (Moderate-resolution Imaging Spectroradiometer) C6 AOD products shows similar spatial distributions in the Jing-Jin-Ji (Beijing–Tianjin–Hebei, China’s mega city cluster) region. The PARASOL AOD retrievals were compared against the AOD measurements of seven AERONET (Aerosol Robotic Network) stations in China to evaluate the performance of the retrieval algorithm. In the fine-particle-dominated regions, lower RMSEs were found at Beijing and Hefei urban stations (0.16 and 0.18, respectively) compared to those at other fine-particle-dominated AERONET stations, which can be attributed to the assumption of surface reflectance spectral shape invariance that has significant advantages in separating the contribution of surface and aerosol scattering in urban areas. For the FMF validation, an RMSE of 0.23, a correlation of 0.57, and a bias of −0.01 were found. These results show that the algorithm performs reasonably in distinguishing the contribution of fine and coarse particles. Full article