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25 pages, 29559 KiB  
Article
CFRANet: Cross-Modal Frequency-Responsive Attention Network for Thermal Power Plant Detection in Multispectral High-Resolution Remote Sensing Images
by Qinxue He, Bo Cheng, Xiaoping Zhang and Yaocan Gan
Remote Sens. 2025, 17(15), 2706; https://doi.org/10.3390/rs17152706 - 5 Aug 2025
Abstract
Thermal Power Plants (TPPs), as widely used industrial facilities for electricity generation, represent a key task in remote sensing image interpretation. However, detecting TPPs remains a challenging task due to their complex and irregular composition. Many traditional approaches focus on detecting compact, small-scale [...] Read more.
Thermal Power Plants (TPPs), as widely used industrial facilities for electricity generation, represent a key task in remote sensing image interpretation. However, detecting TPPs remains a challenging task due to their complex and irregular composition. Many traditional approaches focus on detecting compact, small-scale objects, while existing composite object detection methods are mostly part-based, limiting their ability to capture the structural and textural characteristics of composite targets like TPPs. Moreover, most of them rely on single-modality data, failing to fully exploit the rich information available in remote sensing imagery. To address these limitations, we propose a novel Cross-Modal Frequency-Responsive Attention Network (CFRANet). Specifically, the Modality-Aware Fusion Block (MAFB) facilitates the integration of multi-modal features, enhancing inter-modal interactions. Additionally, the Frequency-Responsive Attention (FRA) module leverages both spatial and localized dual-channel information and utilizes Fourier-based frequency decomposition to separately capture high- and low-frequency components, thereby improving the recognition of TPPs by learning both detailed textures and structural layouts. Experiments conducted on our newly proposed AIR-MTPP dataset demonstrate that CFRANet achieves state-of-the-art performance, with a mAP50 of 82.41%. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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19 pages, 1816 KiB  
Article
Rethinking Infrared and Visible Image Fusion from a Heterogeneous Content Synergistic Perception Perspective
by Minxian Shen, Gongrui Huang, Mingye Ju and Kai-Kuang Ma
Sensors 2025, 25(15), 4658; https://doi.org/10.3390/s25154658 - 27 Jul 2025
Viewed by 269
Abstract
Infrared and visible image fusion (IVIF) endeavors to amalgamate the thermal radiation characteristics from infrared images with the fine-grained texture details from visible images, aiming to produce fused outputs that are more robust and information-rich. Among the existing methodologies, those based on generative [...] Read more.
Infrared and visible image fusion (IVIF) endeavors to amalgamate the thermal radiation characteristics from infrared images with the fine-grained texture details from visible images, aiming to produce fused outputs that are more robust and information-rich. Among the existing methodologies, those based on generative adversarial networks (GANs) have demonstrated considerable promise. However, such approaches are frequently constrained by their reliance on homogeneous discriminators possessing identical architectures, a limitation that can precipitate the emergence of undesirable artifacts in the resultant fused images. To surmount this challenge, this paper introduces HCSPNet, a novel GAN-based framework. HCSPNet distinctively incorporates heterogeneous dual discriminators, meticulously engineered for the fusion of disparate source images inherent in the IVIF task. This architectural design ensures the steadfast preservation of critical information from the source inputs, even when faced with scenarios of image degradation. Specifically, the two structurally distinct discriminators within HCSPNet are augmented with adaptive salient information distillation (ASID) modules, each uniquely structured to align with the intrinsic properties of infrared and visible images. This mechanism impels the discriminators to concentrate on pivotal components during their assessment of whether the fused image has proficiently inherited significant information from the source modalities—namely, the salient thermal signatures from infrared imagery and the detailed textural content from visible imagery—thereby markedly diminishing the occurrence of unwanted artifacts. Comprehensive experimentation conducted across multiple publicly available datasets substantiates the preeminence and generalization capabilities of HCSPNet, underscoring its significant potential for practical deployment. Additionally, we also prove that our proposed heterogeneous dual discriminators can serve as a plug-and-play structure to improve the performance of existing GAN-based methods. Full article
(This article belongs to the Section Sensing and Imaging)
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14 pages, 1829 KiB  
Article
Investigating the Spatial Biases and Temporal Trends in Insect Pollinator Occurrence Data on GBIF
by Ehsan Rahimi and Chuleui Jung
Insects 2025, 16(8), 769; https://doi.org/10.3390/insects16080769 - 26 Jul 2025
Viewed by 424
Abstract
Research in biogeography, ecology, and biodiversity hinges on the availability of comprehensive datasets that detail species distributions and environmental conditions. At the forefront of this endeavor is the Global Biodiversity Information Facility (GBIF). This study focuses on investigating spatial biases and temporal trends [...] Read more.
Research in biogeography, ecology, and biodiversity hinges on the availability of comprehensive datasets that detail species distributions and environmental conditions. At the forefront of this endeavor is the Global Biodiversity Information Facility (GBIF). This study focuses on investigating spatial biases and temporal trends in insect pollinator occurrence data within the GBIF dataset, specifically focusing on three pivotal pollinator groups: bees, hoverflies, and butterflies. Addressing these gaps in GBIF data is essential for comprehensive analyses and informed pollinator conservation efforts. We obtained occurrence data from GBIF for seven bee families, six butterfly families, and the Syrphidae family of hoverflies in 2024. Spatial biases were addressed by eliminating duplicate records with identical latitude and longitude coordinates. Species richness was assessed for each family and country. Temporal trends were examined by tallying annual occurrence records for each pollinator family, and the diversity of data sources within GBIF was evaluated by quantifying unique data publishers. We identified initial occurrence counts of 4,922,390 for bees, 1,703,131 for hoverflies, and 31,700,696 for butterflies, with a substantial portion containing duplicate records. On average, 81.4% of bee data, 77.2% of hoverfly data, and 65.4% of butterfly data were removed post-duplicate elimination for dataset refinement. Our dataset encompassed 9286 unique bee species, 2574 hoverfly species, and 17,895 butterfly species. Our temporal analysis revealed a notable trend in data recording, with 80% of bee and butterfly data collected after 2022, and a similar threshold for hoverflies reached after 2023. The United States, Germany, the United Kingdom, and Sweden consistently emerged as the top countries for occurrence data across all three groups. The analysis of data publishers highlighted iNaturalist.org as a top contributor to bee data. Overall, we uncovered significant biases in the occurrence data of pollinators from GBIF. These biases pose substantial challenges for future research on pollinator ecology and biodiversity conservation. Full article
(This article belongs to the Special Issue Insect Pollinators and Pollination Service Provision)
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21 pages, 3293 KiB  
Article
A Fusion of Entropy-Enhanced Image Processing and Improved YOLOv8 for Smoke Recognition in Mine Fires
by Xiaowei Li and Yi Liu
Entropy 2025, 27(8), 791; https://doi.org/10.3390/e27080791 - 25 Jul 2025
Viewed by 215
Abstract
Smoke appears earlier than flames, so image-based fire monitoring techniques mainly focus on the detection of smoke, which is regarded as one of the effective strategies for preventing the spread of initial fires that eventually evolve into serious fires. Smoke monitoring in mine [...] Read more.
Smoke appears earlier than flames, so image-based fire monitoring techniques mainly focus on the detection of smoke, which is regarded as one of the effective strategies for preventing the spread of initial fires that eventually evolve into serious fires. Smoke monitoring in mine fires faces serious challenges: the underground environment is complex, with smoke and backgrounds being highly integrated and visual features being blurred, which makes it difficult for existing image-based monitoring techniques to meet the actual needs in terms of accuracy and robustness. The conventional ground-based methods are directly used in the underground with a high rate of missed detection and false detection. Aiming at the core problems of mixed target and background information and high boundary uncertainty in smoke images, this paper, inspired by the principle of information entropy, proposes a method for recognizing smoke from mine fires by integrating entropy-enhanced image processing and improved YOLOv8. Firstly, according to the entropy change characteristics of spatio-temporal information brought by smoke diffusion movement, based on spatio-temporal entropy separation, an equidistant frame image differential fusion method is proposed, which effectively suppresses the low entropy background noise, enhances the detail clarity of the high entropy smoke region, and significantly improves the image signal-to-noise ratio. Further, in order to cope with the variable scale and complex texture (high information entropy) of the smoke target, an improvement mechanism based on entropy-constrained feature focusing is introduced on the basis of the YOLOv8m model, so as to more effectively capture and distinguish the rich detailed features and uncertain information of the smoke region, realizing the balanced and accurate detection of large and small smoke targets. The experiments show that the comprehensive performance of the proposed method is significantly better than the baseline model and similar algorithms, and it can meet the demand of real-time detection. Compared with YOLOv9m, YOLOv10n, and YOLOv11n, although there is a decrease in inference speed, the accuracy, recall, average detection accuracy mAP (50), and mAP (50–95) performance metrics are all substantially improved. The precision and robustness of smoke recognition in complex mine scenarios are effectively improved. Full article
(This article belongs to the Section Multidisciplinary Applications)
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21 pages, 4388 KiB  
Article
An Omni-Dimensional Dynamic Convolutional Network for Single-Image Super-Resolution Tasks
by Xi Chen, Ziang Wu, Weiping Zhang, Tingting Bi and Chunwei Tian
Mathematics 2025, 13(15), 2388; https://doi.org/10.3390/math13152388 - 25 Jul 2025
Viewed by 280
Abstract
The goal of single-image super-resolution (SISR) tasks is to generate high-definition images from low-quality inputs, with practical uses spanning healthcare diagnostics, aerial imaging, and surveillance systems. Although cnns have considerably improved image reconstruction quality, existing methods still face limitations, including inadequate restoration of [...] Read more.
The goal of single-image super-resolution (SISR) tasks is to generate high-definition images from low-quality inputs, with practical uses spanning healthcare diagnostics, aerial imaging, and surveillance systems. Although cnns have considerably improved image reconstruction quality, existing methods still face limitations, including inadequate restoration of high-frequency details, high computational complexity, and insufficient adaptability to complex scenes. To address these challenges, we propose an Omni-dimensional Dynamic Convolutional Network (ODConvNet) tailored for SISR tasks. Specifically, ODConvNet comprises four key components: a Feature Extraction Block (FEB) that captures low-level spatial features; an Omni-dimensional Dynamic Convolution Block (DCB), which utilizes a multidimensional attention mechanism to dynamically reweight convolution kernels across spatial, channel, and kernel dimensions, thereby enhancing feature expressiveness and context modeling; a Deep Feature Extraction Block (DFEB) that stacks multiple convolutional layers with residual connections to progressively extract and fuse high-level features; and a Reconstruction Block (RB) that employs subpixel convolution to upscale features and refine the final HR output. This mechanism significantly enhances feature extraction and effectively captures rich contextual information. Additionally, we employ an improved residual network structure combined with a refined Charbonnier loss function to alleviate gradient vanishing and exploding to enhance the robustness of model training. Extensive experiments conducted on widely used benchmark datasets, including DIV2K, Set5, Set14, B100, and Urban100, demonstrate that, compared with existing deep learning-based SR methods, our ODConvNet method improves Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the visual quality of SR images is also improved. Ablation studies further validate the effectiveness and contribution of each component in our network. The proposed ODConvNet offers an effective, flexible, and efficient solution for the SISR task and provides promising directions for future research. Full article
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20 pages, 2792 KiB  
Article
Capturing High-Frequency Harmonic Signatures for NILM: Building a Dataset for Load Disaggregation
by Farid Dinar, Sébastien Paris and Éric Busvelle
Sensors 2025, 25(15), 4601; https://doi.org/10.3390/s25154601 - 25 Jul 2025
Viewed by 266
Abstract
Advanced Non-Intrusive Load Monitoring (NILM) research is important to help reduce energy consumption. Very-low-frequency approaches have traditionally faced challenges in separating appliance uses due to low discriminative information. The richer signatures available in high-frequency electrical data include many harmonic orders that have the [...] Read more.
Advanced Non-Intrusive Load Monitoring (NILM) research is important to help reduce energy consumption. Very-low-frequency approaches have traditionally faced challenges in separating appliance uses due to low discriminative information. The richer signatures available in high-frequency electrical data include many harmonic orders that have the potential to advance disaggregation. This has been explored to some extent, but not comprehensively due to a lack of an appropriate public dataset. This paper presents the development of a cost-effective energy monitoring system scalable for multiple entries while producing detailed measurements. We will detail our approach to creating a NILM dataset comprising both aggregate loads and individual appliance measurements, all while ensuring that the dataset is reproducible and accessible. Ultimately, the dataset can be used to validate NILM, and we show through the use of machine learning techniques that high-frequency features improve disaggregation accuracy when compared with traditional methods. This work addresses a critical gap in NILM research by detailing the design and implementation of a data acquisition system capable of generating rich and structured datasets that support precise energy consumption analysis and prepare the essential materials for advanced, real-time energy disaggregation and smart energy management applications. Full article
(This article belongs to the Section Intelligent Sensors)
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16 pages, 2308 KiB  
Article
Reconstructing of Satellite-Derived CO2 Using Multiple Environmental Variables—A Case Study in the Provinces of Huai River Basin, China
by Yuxin Zhu, Ying Zhang, Linping Zhu and Jinzong Zhang
Atmosphere 2025, 16(8), 903; https://doi.org/10.3390/atmos16080903 - 24 Jul 2025
Viewed by 215
Abstract
The introduction of the ”dual carbon” target has increased the need for products that can accurately measure carbon dioxide levels, reflecting the rising demand. Due to challenges in achieving the required spatiotemporal resolution, accuracy, and spatial continuity with current carbon dioxide concentration products, [...] Read more.
The introduction of the ”dual carbon” target has increased the need for products that can accurately measure carbon dioxide levels, reflecting the rising demand. Due to challenges in achieving the required spatiotemporal resolution, accuracy, and spatial continuity with current carbon dioxide concentration products, it is essential to explore methods for obtaining carbon dioxide concentration products with completeness in space and time. Based on the 2018 OCO-2 carbon dioxide products and environmental variables such as vegetation coverage (FVC, LAI), net primary productivity (NPP), relative humidity (RH), evapotranspiration (ET), temperature (T) and wind (U, V), this study constructed a multiple regression model to obtain the spatial continuous carbon dioxide concentration products in the provinces of Huai River Basin. Using indicators such as correlation coefficient, root mean square error (RMSE), local variance, and percentage of valid pixels, the performance of model was validated. The validation results are shown as follows: (1) Among the selected environmental variables, the primary factors affecting the spatiotemporal distribution of carbon dioxide concentration are ET, LAI, FVC, NPP, T, U, and RH. (2) Compared with the OCO-2 carbon dioxide products, the percentage of valid pixels of the reconstructed carbon dioxide concentration data increased from less than 1% to over 90%. (3) The local variance in reconstructed data was significantly larger than that of original OCO-2 CO2 products. (4) The average monthly RMSE is 2.69. Therefore, according to the model developed in this study, we can obtain a carbon dioxide concentration dataset that is spatially complete, meets precision requirements, and is rich in local detail information, which can better reflect the spatial pattern of carbon dioxide concentration and can be used to examine the carbon cycle between the terrestrial environment, biosphere, and atmosphere. Full article
(This article belongs to the Section Air Quality)
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26 pages, 9183 KiB  
Review
Application of Image Computing in Non-Destructive Detection of Chinese Cuisine
by Xiaowei Huang, Zexiang Li, Zhihua Li, Jiyong Shi, Ning Zhang, Zhou Qin, Liuzi Du, Tingting Shen and Roujia Zhang
Foods 2025, 14(14), 2488; https://doi.org/10.3390/foods14142488 - 16 Jul 2025
Viewed by 506
Abstract
Food quality and safety are paramount in preserving the culinary authenticity and cultural integrity of Chinese cuisine, characterized by intricate ingredient combinations, diverse cooking techniques (e.g., stir-frying, steaming, and braising), and region-specific flavor profiles. Traditional non-destructive detection methods often struggle with the unique [...] Read more.
Food quality and safety are paramount in preserving the culinary authenticity and cultural integrity of Chinese cuisine, characterized by intricate ingredient combinations, diverse cooking techniques (e.g., stir-frying, steaming, and braising), and region-specific flavor profiles. Traditional non-destructive detection methods often struggle with the unique challenges posed by Chinese dishes, including complex textural variations in staple foods (e.g., noodles, dumplings), layered seasoning compositions (e.g., soy sauce, Sichuan peppercorns), and oil-rich cooking media. This study pioneers a hyperspectral imaging framework enhanced with domain-specific deep learning algorithms (spatial–spectral convolutional networks with attention mechanisms) to address these challenges. Our approach effectively deciphers the subtle spectral fingerprints of Chinese-specific ingredients (e.g., fermented black beans, lotus root) and quantifies critical quality indicators, achieving an average classification accuracy of 97.8% across 15 major Chinese dish categories. Specifically, the model demonstrates high precision in quantifying chili oil content in Mapo Tofu with a Mean Absolute Error (MAE) of 0.43% w/w and assessing freshness gradients in Cantonese dim sum (Shrimp Har Gow) with a classification accuracy of 95.2% for three distinct freshness levels. This approach leverages the detailed spectral information provided by hyperspectral imaging to automate the classification and detection of Chinese dishes, significantly improving both the accuracy of image-based food classification by >15 percentage points compared to traditional RGB methods and enhancing food quality safety assessment. Full article
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22 pages, 7562 KiB  
Article
FIGD-Net: A Symmetric Dual-Branch Dehazing Network Guided by Frequency Domain Information
by Luxia Yang, Yingzhao Xue, Yijin Ning, Hongrui Zhang and Yongjie Ma
Symmetry 2025, 17(7), 1122; https://doi.org/10.3390/sym17071122 - 13 Jul 2025
Viewed by 361
Abstract
Image dehazing technology is a crucial component in the fields of intelligent transportation and autonomous driving. However, most existing dehazing algorithms only process images in the spatial domain, failing to fully exploit the rich information in the frequency domain, which leads to residual [...] Read more.
Image dehazing technology is a crucial component in the fields of intelligent transportation and autonomous driving. However, most existing dehazing algorithms only process images in the spatial domain, failing to fully exploit the rich information in the frequency domain, which leads to residual haze in the images. To address this issue, we propose a novel Frequency-domain Information Guided Symmetric Dual-branch Dehazing Network (FIGD-Net), which utilizes the spatial branch to extract local haze features and the frequency branch to capture the global haze distribution, thereby guiding the feature learning process in the spatial branch. The FIGD-Net mainly consists of three key modules: the Frequency Detail Extraction Module (FDEM), the Dual-Domain Multi-scale Feature Extraction Module (DMFEM), and the Dual-Domain Guidance Module (DGM). First, the FDEM employs the Discrete Cosine Transform (DCT) to convert the spatial domain into the frequency domain. It then selectively extracts high-frequency and low-frequency features based on predefined proportions. The high-frequency features, which contain haze-related information, are correlated with the overall characteristics of the low-frequency features to enhance the representation of haze attributes. Next, the DMFEM utilizes stacked residual blocks and gradient feature flows to capture local detail features. Specifically, frequency-guided weights are applied to adjust the focus of feature channels, thereby improving the module’s ability to capture multi-scale features and distinguish haze features. Finally, the DGM adjusts channel weights guided by frequency information. This smooths out redundant signals and enables cross-branch information exchange, which helps to restore the original image colors. Extensive experiments demonstrate that the proposed FIGD-Net achieves superior dehazing performance on multiple synthetic and real-world datasets. Full article
(This article belongs to the Section Computer)
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17 pages, 7786 KiB  
Article
Video Coding Based on Ladder Subband Recovery and ResGroup Module
by Libo Wei, Aolin Zhang, Lei Liu, Jun Wang and Shuai Wang
Entropy 2025, 27(7), 734; https://doi.org/10.3390/e27070734 - 8 Jul 2025
Viewed by 338
Abstract
With the rapid development of video encoding technology in the field of computer vision, the demand for tasks such as video frame reconstruction, denoising, and super-resolution has been continuously increasing. However, traditional video encoding methods typically focus on extracting spatial or temporal domain [...] Read more.
With the rapid development of video encoding technology in the field of computer vision, the demand for tasks such as video frame reconstruction, denoising, and super-resolution has been continuously increasing. However, traditional video encoding methods typically focus on extracting spatial or temporal domain information, often facing challenges of insufficient accuracy and information loss when reconstructing high-frequency details, edges, and textures of images. To address this issue, this paper proposes an innovative LadderConv framework, which combines discrete wavelet transform (DWT) with spatial and channel attention mechanisms. By progressively recovering wavelet subbands, it effectively enhances the video frame encoding quality. Specifically, the LadderConv framework adopts a stepwise recovery approach for wavelet subbands, first processing high-frequency detail subbands with relatively less information, then enhancing the interaction between these subbands, and ultimately synthesizing a high-quality reconstructed image through inverse wavelet transform. Moreover, the framework introduces spatial and channel attention mechanisms, which further strengthen the focus on key regions and channel features, leading to notable improvements in detail restoration and image reconstruction accuracy. To optimize the performance of the LadderConv framework, particularly in detail recovery and high-frequency information extraction tasks, this paper designs an innovative ResGroup module. By using multi-layer convolution operations along with feature map compression and recovery, the ResGroup module enhances the network’s expressive capability and effectively reduces computational complexity. The ResGroup module captures multi-level features from low level to high level and retains rich feature information through residual connections, thus improving the overall reconstruction performance of the model. In experiments, the combination of the LadderConv framework and the ResGroup module demonstrates superior performance in video frame reconstruction tasks, particularly in recovering high-frequency information, image clarity, and detail representation. Full article
(This article belongs to the Special Issue Rethinking Representation Learning in the Age of Large Models)
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15 pages, 6065 KiB  
Article
Characteristics of Microorganisms and Origins of Organic Matter in Permian Shale in Northwestern Sichuan Basin, South China
by Yuying Zhang, Baojian Shen, Bo Gao, Dongjun Feng, Pengwei Wang, Min Li, Yifei Li and Yang Liu
Processes 2025, 13(7), 2080; https://doi.org/10.3390/pr13072080 - 1 Jul 2025
Viewed by 297
Abstract
Permian shale gas, a resource-rich energy source, has garnered significant attention in recent years regarding its organic matter enrichment characteristics. This study conducted detailed observations via scanning electron microscopy (SEM) and optical microscopy to clarify the differences in the types and assemblages of [...] Read more.
Permian shale gas, a resource-rich energy source, has garnered significant attention in recent years regarding its organic matter enrichment characteristics. This study conducted detailed observations via scanning electron microscopy (SEM) and optical microscopy to clarify the differences in the types and assemblages of hydrocarbon-generating organisms across Permian shale formations in Northwestern Sichuan, as well as to determine the characteristics of organic matter sources. The types and combinations of hydrocarbon-generating organisms in the Gufeng Formation, Wujiaping Formation, and Dalong Formation in Northwestern Sichuan are systematically summarized. Based on this information, the primary sources of organic matter in the Permian shale were analyzed. Hydrocarbon-generating organisms in the Permian shales of the study area are predominantly acritarchs (a type of planktonic algae), followed by higher plants and green algae. In the Gufeng Formation, acritarchs constituted the vast majority of hydrocarbon-generating organisms, with smaller amounts of higher plants and green algae. At the bottom of the Wujiaping Formation, the relative acritarch content decreases significantly, while that of higher plants substantially increases. In the Dalong Formation, acritarchs regain dominance, and higher plants decline, resembling the Gufeng Formation in microorganism composition. The relative content of green algae shows minimal variation across all layers. Overall, the organic matter sources of Permian shale in the study area were mainly acritarchs (derived from planktonic algae), followed by green algae, and terrestrial higher plants. During the Gufeng Formation period, the sea level was relatively high. The Kaijiang–Liangping Trough in Northwestern Sichuan was generally a siliceous deep shelf. The main source of organic matter was aquatic planktonic algae, containing a small amount of terrigenous input. At the bottom of the Wujiaping Formation, the sea level was relatively low, resulting in the overall coastal marsh environment of the Kaijiang–Liangping Trough, which was characterized by mixed organic matter sources, due to an increase in terrigenous organic matter content. The sedimentary environment and organic matter sources of the Dalong Formation were similar to those of the Gufeng Formation. This research can provide a theoretical basis for exploration and development of Permian shale gas. Full article
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20 pages, 1741 KiB  
Article
SAR-DeCR: Latent Diffusion for SAR-Fused Thick Cloud Removal
by Meilin Wang, Shihao Hu, Yexing Song and Yukai Shi
Remote Sens. 2025, 17(13), 2241; https://doi.org/10.3390/rs17132241 - 30 Jun 2025
Viewed by 403
Abstract
The current methods for removing thick clouds from remote-sensing images face significant limitations, including the integration of thick cloud images with synthetic aperture radar (SAR) ground information, the provision of meaningful guidance for SAR ground data, and the accurate reconstruction of textures in [...] Read more.
The current methods for removing thick clouds from remote-sensing images face significant limitations, including the integration of thick cloud images with synthetic aperture radar (SAR) ground information, the provision of meaningful guidance for SAR ground data, and the accurate reconstruction of textures in cloud-covered regions. To overcome these challenges, we introduce SAR-DeCR, a novel method for thick cloud removal in satellite remote-sensing images. SAR-DeCR utilizes a diffusion model combined with the transformer architecture to synthesize accurate texture details guided by SAR ground information. The method is structured into three distinct phases: coarse cloud removal (CCR), SAR-Fusion (SAR-F) and cloud-free diffusion (CF-D), aimed at enhancing the effectiveness of the thick cloud removal. In CCR, we significantly employ the transformer’s capability for long-range information interaction, which significantly strengthens the cloud removal process. In order to overcome the problem of missing ground information after cloud removal and ensure that the ground information produced is consistent with SAR data, we introduced SAR-F, a module designed to incorporate the rich ground information in synthetic aperture radar (SAR) into the output of CCR. Additionally, to achieve superior texture reconstruction, we introduce prior supervision based on the output of the coarse cloud removal, using a pre-trained visual-text diffusion model named cloud-free diffusion (CF-D). This diffusion model is encouraged to follow the visual prompts, thus producing a visually appealing, high-quality result. The effectiveness and superiority of SAR-DeCR are demonstrated through qualitative and quantitative experiments, comparing it with other state-of-the-art (SOTA) thick cloud removal methods on the large-scale SEN12MS-CR dataset. Full article
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27 pages, 4947 KiB  
Article
From Coarse to Crisp: Enhancing Tree Species Maps with Deep Learning and Satellite Imagery
by Taebin Choe, Seungpyo Jeon, Byeongcheol Kim and Seonyoung Park
Remote Sens. 2025, 17(13), 2222; https://doi.org/10.3390/rs17132222 - 28 Jun 2025
Viewed by 431
Abstract
Accurate, detailed, and up-to-date tree species distribution information is essential for effective forest management and environmental research. However, existing tree species maps face limitations in resolution and update cycle, making it difficult to meet modern demands. To overcome these limitations, this study proposes [...] Read more.
Accurate, detailed, and up-to-date tree species distribution information is essential for effective forest management and environmental research. However, existing tree species maps face limitations in resolution and update cycle, making it difficult to meet modern demands. To overcome these limitations, this study proposes a novel framework that utilizes existing medium-resolution national tree species maps as ‘weak labels’ and fuses multi-temporal Sentinel-2 and PlanetScope satellite imagery data. Specifically, a super-resolution (SR) technique, using PlanetScope imagery as a reference, was first applied to Sentinel-2 data to enhance its resolution to 2.5 m. Then, these enhanced Sentinel-2 bands were combined with PlanetScope bands to construct the final multi-spectral, multi-temporal input data. Deep learning (DL) model training data was constructed by strategically sampling information-rich pixels from the national tree species map. Applying the proposed methodology to Sobaeksan and Jirisan National Parks in South Korea, the performance of various machine learning (ML) and deep learning (DL) models was compared, including traditional ML (linear regression, random forest) and DL architectures (multilayer perceptron (MLP), spectral encoder block (SEB)—linear, and SEB-transformer). The MLP model demonstrated optimal performance, achieving over 85% overall accuracy (OA) and more than 81% accuracy in classifying spectrally similar and difficult-to-distinguish species, specifically Quercus mongolica (QM) and Quercus variabilis (QV). Furthermore, while spectral and temporal information were confirmed to contribute significantly to tree species classification, the contribution of spatial (texture) information was experimentally found to be limited at the 2.5 m resolution level. This study presents a practical method for creating high-resolution tree species maps scalable to the national level by fusing existing tree species maps with Sentinel-2 and PlanetScope imagery without requiring costly separate field surveys. Its significance lies in establishing a foundation that can contribute to various fields such as forest resource management, biodiversity conservation, and climate change research. Full article
(This article belongs to the Special Issue Digital Modeling for Sustainable Forest Management)
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12 pages, 10683 KiB  
Article
A Multi-Analytical Approach to Investigate Fresco Paintings in a Hypogeum Environment
by Chiara Gallo, Oriana Motta, Carmine Napoli, Antonio Faggiano, Maria Ricciardi, Rosa Fiorillo, Eduardo Caliano and Antonio Proto
Appl. Sci. 2025, 15(13), 7286; https://doi.org/10.3390/app15137286 - 27 Jun 2025
Viewed by 356
Abstract
In recent decades, there has been an increase in the development of non-invasive and non-destructive analytical techniques in the field of cultural heritage. The present study aims to characterize the frescoes in the hypogeum environment of the San Pietro a Corte complex in [...] Read more.
In recent decades, there has been an increase in the development of non-invasive and non-destructive analytical techniques in the field of cultural heritage. The present study aims to characterize the frescoes in the hypogeum environment of the San Pietro a Corte complex in Salerno (Campania, Italy) through a multi-analytical approach that couples Infrared Reflectography with X-Ray Fluorescence Spectrometry. Thermographic and hygrometric measurements were also performed to evaluate their state of conservation in relation to environmental parameters such as relative humidity and temperature at the frescoed walls. Spectroscopic investigations revealed a predominant use of natural pigments—chiefly iron-rich earths—and uncovered details invisible to the naked eye that aid art historians in refining stylistic attributions. Hygrometric data showed that the central zones of the frescoes retain the highest moisture levels, underscoring the need for a carefully tailored conservation plan. Overall, this multi-analytical methodology provides important information that enables conservators and restorers to understand both the materials and the preservation requirements of these artworks from a scientific and conservation perspective. Full article
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18 pages, 1148 KiB  
Article
A Multi-Scale Unsupervised Feature Extraction Network with Structured Layer-Wise Decomposition
by Yusuf Şevki Günaydın and Baha Şen
Appl. Sci. 2025, 15(13), 7194; https://doi.org/10.3390/app15137194 - 26 Jun 2025
Viewed by 328
Abstract
Recent developments in deep learning have underscored prizing effective feature extraction in scenarios with limited or unlabeled data. This study introduces a novel unsupervised multi-scale feature extraction framework based on a multi-branch auto-encoder architecture. The proposed method decomposes input images into smooth, detailed [...] Read more.
Recent developments in deep learning have underscored prizing effective feature extraction in scenarios with limited or unlabeled data. This study introduces a novel unsupervised multi-scale feature extraction framework based on a multi-branch auto-encoder architecture. The proposed method decomposes input images into smooth, detailed and residual components, using variational loss functions to ensure that each branch captures distinct and non-overlapping representations. This decomposition enhances the information richness of input data while preserving its structural integrity, making it especially beneficial for grayscale or low-resolution images. Experimental results on classification and image segmentation tasks show that the proposed method enhances model performance by enriching input representations. Its architecture is scalable and adaptable, making it applicable to a wide range of machine learning tasks beyond image classification and segmentation. These findings highlight the proposed method’s utility as a robust, general-purpose solution for unsupervised feature extraction and multi-scale representation learning. Full article
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