Search Results (171)

Pansharpening plays a crucial role in remote sensing applications, as it enables the generation of high-spatial-resolution multispectral images that simultaneously preserve spatial and spectral information. However, most current methods struggle to preserve local textures and exploit spectral correlations across bands while modeling nonlocal information in source images. To address these issues, we propose a cascaded local–nonlocal pansharpening network (CLNNet) that progressively integrates local and nonlocal features through stacked Progressive Local–Nonlocal Fusion (PLNF) modules. This cascaded design allows CLNNet to gradually refine spatial–spectral information. Each PLNF module combines Adaptive Channel-Kernel Convolution (ACKC), which extracts local spatial features using channel-specific convolution kernels, and a Multi-Scale Large-Kernel Attention (MSLKA) module, which leverages multi-scale large-kernel convolutions with varying receptive fields to capture nonlocal information. The attention mechanism in MSLKA enhances spatial–spectral feature representation by integrating information across multiple dimensions. Extensive experiments on the GaoFen-2, QuickBird, and WorldView-3 datasets demonstrate that the proposed method outperforms state-of-the-art methods in quantitative metrics and visual quality. Full article

High-resolution remote sensing imagery offers detailed spatial and semantic insights into the Earth’s surface, yet its classification remains hindered by the limited availability of labeled data, primarily due to the substantial expense and time required for manual annotation. To overcome this challenge, we propose a hybrid semi-supervised tri-training framework that integrates traditional classification methods with a lightweight convolutional neural network. By combining heterogeneous learners with complementary strengths, the framework iteratively assigns pseudo-labels to unlabeled samples and collaboratively refines model performance in a co-training manner. Additionally, a landscape-metric-guided relearning module is introduced to incorporate spatial configuration and land cover composition, further enhancing the framework’s representational capacity and classification robustness. Experiments were conducted on four high-resolution multispectral datasets (QuickBird (QB), WorldView-2 (WV-2), GeoEye-1 (GE-1), and ZY-3) covering diverse land-cover types and spatial resolutions. The results demonstrate that the proposed method surpasses state-of-the-art baselines by 1.5–10% while generating more spatially coherent classification maps. Full article

Pansharpening fuses multispectral (MS) and panchromatic (PAN) remote sensing images to generate outputs with high spatial resolution and spectral fidelity. Nevertheless, conventional methods relying primarily on convolutional neural networks or unimodal fusion strategies frequently fail to bridge the sensor modality gap between MS and PAN data. Consequently, spectral distortion and spatial degradation often occur, limiting high-precision downstream applications. To address these issues, this work proposes a Hybrid Spectral–Structural Transformer Network (HSSTN) that enhances multi-level collaboration through comprehensive modelling of spectral–structural feature complementarity. Specifically, the HSSTN implements a three-tier fusion framework. First, an asymmetric dual-stream feature extractor employs a residual block with channel attention (RBCA) in the MS branch to strengthen spectral representation, while a Transformer architecture in the PAN branch extracts high-frequency spatial details, thereby reducing modality discrepancy at the input stage. Subsequently, a target-driven hierarchical fusion network utilises progressive crossmodal attention across scales, ranging from local textures to multi-scale structures, to enable efficient spectral–structural aggregation. Finally, a novel collaborative optimisation loss function preserves spectral integrity while enhancing structural details. Comprehensive experiments conducted on QuickBird, GaoFen-2, and WorldView-3 datasets demonstrate that HSSTN outperforms existing methods in both quantitative metrics and visual quality. Consequently, the resulting images exhibit sharper details and fewer spectral artefacts, showcasing significant advantages in high-fidelity remote sensing image fusion. Full article

Pansharpening is essential for remote sensing applications requiring high spatial and spectral resolution. In this paper, we propose a novel Singular Spectrum Analysis-Enhanced Generative Adversarial Network (SSA-GAN) for multispectral pansharpening. We designed SSA modules within the generator, enabling more effective extraction and utilization of spectral features. Additionally, we introduce Pareto optimization to the nonreference loss function to improve the overall performance. We conducted comparative experiments on two representative datasets, QuickBird and Gaofen-2 (GF-2). On the GF-2 dataset, the Peak Signal-to-Noise Ratio (PSNR) reached 30.045 and Quality with No Reference (QNR) achieved 0.920, while on the QuickBird dataset, PSNR and QNR were 24.262 and 0.817, respectively. These results indicate that the proposed method can generate high-quality pansharpened images with enhanced spatial and spectral resolution. Full article

In recent years, the pansharpening methods based on deep learning show great advantages. However, these methods are still inadequate in considering the differences and correlations between multispectral (MS) and panchromatic (PAN) images. In response to the issue, we propose a novel dual-branch pansharpening network with high-frequency component enhancement and a multi-scale skip connection. First, to enhance the correlations, the high-frequency branch consists of the high-frequency component enhancement module (HFCEM), which effectively enhances the high-frequency components through the multi-scale block (MSB), thereby obtaining the corresponding high-frequency weights to accurately capture the high-frequency information in MS and PAN images. Second, to address the differences, the low-frequency branch consists of the multi-scale skip connection module (MSSCM), which comprehensively captures the multi-scale features from coarse to fine through multi-scale convolution, and it effectively fuses these multilevel features through the designed skip connection mechanism to fully extract the low-frequency information from MS and PAN images. Finally, the qualitative and quantitative experiments are performed on the GaoFen-2, QuickBird, and WorldView-3 datasets. The results show that the proposed method outperforms the state-of-the-art pansharpening methods. Full article

In power grid surveying, it is often necessary to fuse panchromatic and multispectral imagery for the design of power lines. Despite the abundance of deep learning networks for fusing these images, the results often suffer from spectral information loss or structural blurring. This study introduces a fusion model specifically tailored for power grid surveying that significantly enhances the representation of spatial–spectral features in remote sensing images. The model comprises three main modules: a TransforRS-Mamba module that integrates the sequence processing capabilities of the Mamba model with the attention mechanism of the Transformer to effectively merge spatial and spectral features; an improved spatial proximity-aware attention mechanism (SPPAM) that utilizes a spatial constraint matrix to greatly enhance the recognition of complex object relationships; and an optimized spatial proximity-constrained gated fusion module (SPCGF) that integrates spatial proximity constraints with residual connections to boost the recognition accuracy of key object features. To validate the effectiveness of the proposed method, extensive comparative and ablation experiments were conducted on GF-2 satellite images and the QuickBird (QB) dataset. Both qualitative and quantitative analyses indicate that our method outperforms 11 existing methods in terms of fusion effectiveness, particularly in reducing spectral distortion and spatial detail loss. However, the model’s generalization performance across different data sources and environmental conditions has yet to be evaluated. Future research will explore the integration of various satellite datasets and assess the model’s performance in diverse environmental contexts. Full article

In the field of remote sensing image processing, pansharpening technology stands as a critical advancement. This technology aims to enhance multispectral images that possess low resolution by integrating them with high-spatial-resolution panchromatic images, ultimately producing multispectral images with high resolution that are abundant in both spatial and spectral details. Thus, there remains potential for improving the quality of both the spectral and spatial domains of the fused images based on deep-learning-based pansharpening methods. This work proposes a new method for the task of pansharpening: the Multi-Stage Progressive Pansharpening Network with Detail Injection with Redundancy Reduction Mechanism (MSPPN-DIRRM). This network is divided into three levels, each of which is optimized for the extraction of spectral and spatial data at different scales. Particular spectral feature and spatial detail extraction modules are used at each stage. Moreover, a new image reconstruction module named the DRRM is introduced in this work; it eliminates both spatial and channel redundancy and improves the fusion quality. The effectiveness of the proposed model is further supported by experimental results using both simulated data and real data from the QuickBird, GaoFen1, and WorldView2 satellites; these results show that the proposed model outperforms deep-learning-based methods in both visual and quantitative assessments. Among various evaluation metrics, performance improves by 0.92–18.7% compared to the latest methods. Full article

Avian botulism is a paralytic disease due to the ingestion of botulinum neurotoxins (BoNT) produced by anaerobic, sporigenic bacteria (notably, Clostridium botulinum). Wild waterbirds worldwide are affected with variable recurrence and severity, and organic material decaying in wetland habitats may constitute a suitable substrate for the replication of clostridia strains producing BoNT in conditions of high temperatures and the absence of oxygen. Here, we describe a large outbreak of avian botulism that occurred in the Valle Mandriole protected area of northeastern Italy (VM). After the recovery in late summer of a few duck carcasses that molecularly tested positive for BoNT-producing clostridia, in October 2019, the avian botulism escalation led to a total of 2367 birds being recovered (2158 carcasses and 209 sick birds). Among these, 2365/2367 were waterbirds, with ducks accounting for 91.8% of the total (2173/2367) and green-winged teals representing 93.5% of the ducks. After the quick collection of dead and sick birds (from 4 to 11 October 2019) and the flooding of the VM wetland (from 5 to 12 October 2019), the 2019 botulism emergency apparently ended. Following two water inputs in May and July 2020, only one pooled sample obtained from 16 bird carcasses found that year in VM tested positive for clostridia type C by real-time PCR, whereas, after to the implementation of measures deterring the bird’s presence, new avian botulism cases—due to clostridia type C and C/D, according to molecular and animal-model tests of confirmation—led to the collection of 176 waterbirds (82 carcasses and 94 sick ducks) and 16 waterbirds (9 carcasses and 7 sick ducks) in the summers 2021 and 2022, respectively. In conclusion, the prevention, management, and control of the disease rely on habitat management, the quick and careful collection/removal of animal carcasses, and the regular monitoring and surveillance of live and dead birds. Full article

Deep learning methods paired with sidescan sonar (SSS) are commonly used in underwater search-and-rescue operations for drowning victims, wrecks, and airplanes. However, these techniques are primarily used to detect mine-like objects and are rarely applied to identifying features in dynamic dredge pit environments. In this study, we present a Sandy Point dredge pit (SPDP) dataset, in which high-resolution SSS data were collected from the west flank of the Mississippi bird-foot delta on the Louisiana inner shelf. This dataset contains a total of 385 SSS images. We then introduce a new Effective Geomorphology Classification model (EGC). Through ablation studies, we analyze the utility of transfer learning on different model architectures and the impact of data augmentations on model performance. This EGC model makes geomorphic feature identification in dredge pit environments, which requires extensive experience and professional knowledge, a quick and efficient task. The combination of SSS images and the EGC model is a cost-effective and valuable toolkit for hazard monitoring in marine dredge pit environments. The SPDP SSS image dataset, especially the feature of pit walls without a rotational slump, is also valuable for other machine learning models. Full article

Pansharpening is the fusion of panchromatic images and multispectral images to obtain images with high spatial resolution and high spectral resolution, which have a wide range of applications. At present, methods based on deep learning can fit the nonlinear features of images and achieve excellent image quality; however, the images generated with supervised learning approaches lack real-world applicability. Therefore, in this study, we propose an unsupervised pansharpening method based on a generative adversarial network. Considering the fine tubular structures in remote sensing images, a dense connection attention module is designed based on dynamic snake convolution to recover the details of spatial information. In the stage of image fusion, the fusion of features in groups is applied through the cross-scale attention fusion module. Moreover, skip layers are implemented at different scales to integrate significant information, thus improving the objective index values and visual appearance. The loss function contains four constraints, allowing the model to be effectively trained without reference images. The experimental results demonstrate that the proposed method outperforms other widely accepted state-of-the-art methods on the QuickBird and WorldView2 data sets. Full article

We demonstrate a highly sensitive plasmon-enhanced fluorescence sensor to detect antibodies to Cytomegalovirus (CMV), using their specific interaction with a peptide identified through in silico methods. The results show high promise for sensor miniaturization, ease of spatial multiplexing, high sensitivity, and quick response times. The developments are readily applicable to detect antibodies to range of other viruses (e.g., SARS-CoV-2 virus, Bird and Swine Flu). Full article

Understanding the evolving hydrological characteristics of landslide-induced barrier lakes is crucial for flood control, forecasting, early warning, and safety measures in reservoir areas. This study examines the changes in the hydrological characteristics of the Attabad landslide-dammed lake over the past decade after the occurrence of the landslide, focusing on lake area dynamics and sediment concentration. High-resolution satellite images from QuickBird, Pleiades, and WorldView2 over seven periods were analyzed. The findings indicate that the lake area has gradually decreased, with the center of mass shifting towards the lake dam, indicating a trend towards stability. The suspended sediment in the barrier lake is distributed in a strip running from north to south, then northeast to southwest, with the sediment concentration decreasing from the lake entrance to the dam and from the lake bank to the center. Over time, the average sediment concentration has decreased from 2010 to 2020, with higher concentrations in summer than in winter. Notably, during the 2017–2020 period, the lower-middle parts of the lake experienced a higher sediment concentration, while the dam area witnessed lower concentrations, thereby reducing the sediment impact on the dam. Furthermore, the sediment content in the middle of the dammed lake is relatively high, which may lead to the formation of a new dammed dam in the middle and the division of the original dammed lake into two smaller lakes, which will affect the stability of the dammed lake. Full article

Drones are an increasingly popular choice for wildlife surveys due to their versatility, quick response capabilities, and ability to access remote areas while covering large regions. A novel application presented here is to combine drone imagery with neural networks to assess mortality within a bird colony. Since 2021, Highly Pathogenic Avian Influenza (HPAI) has caused significant bird mortality in the UK, mainly affecting aquatic bird species. The world’s largest northern gannet colony on Scotland’s Bass Rock experienced substantial losses in 2022 due to the outbreak. To assess the impact, RGB imagery of Bass Rock was acquired in both 2022 and 2023 by deploying a drone over the island for the first time. A deep learning neural network was subsequently applied to the data to automatically detect and count live and dead gannets, providing population estimates for both years. The model was trained on the 2022 dataset and achieved a mean average precision (mAP) of 37%. Application of the model predicted 18,220 live and 3761 dead gannets for 2022, consistent with NatureScot’s manual count of 21,277 live and 5035 dead gannets. For 2023, the model predicted 48,455 live and 43 dead gannets, and the manual count carried out by the Scottish Seabird Centre and UK Centre for Ecology and Hydrology (UKCEH) of the same area gave 51,428 live and 23 dead gannets. This marks a promising start to the colony’s recovery with a population increase of 166% determined by the model. The results presented here are the first known application of deep learning to detect dead birds from drone imagery, showcasing the methodology’s swift and adaptable nature to not only provide ongoing monitoring of seabird colonies and other wildlife species but also to conduct mortality assessments. As such, it could prove to be a valuable tool for conservation purposes. Full article

Achieving a balance between spectral resolution and spatial resolution in multi-spectral remote sensing images is challenging due to physical constraints. Consequently, pan-sharpening technology was developed to address this challenge. While significant progress was recently achieved in deep-learning-based pan-sharpening techniques, most existing deep learning approaches face two primary limitations: (1) convolutional neural networks (CNNs) struggle with long-range dependency issues, and (2) significant detail loss during deep network training. Moreover, despite these methods’ pan-sharpening capabilities, their generalization to full-sized raw images remains problematic due to scaling disparities, rendering them less practical. To tackle these issues, we introduce in this study a multi-spectral remote sensing image fusion network, termed TAMINet, which leverages a two-stream coordinate attention mechanism and multi-detail injection. Initially, a two-stream feature extractor augmented with the coordinate attention (CA) block is employed to derive modal-specific features from low-resolution multi-spectral (LRMS) images and panchromatic (PAN) images. This is followed by feature-domain fusion and pan-sharpening image reconstruction. Crucially, a multi-detail injection approach is incorporated during fusion and reconstruction, ensuring the reintroduction of details lost earlier in the process, which minimizes high-frequency detail loss. Finally, a novel hybrid loss function is proposed that incorporates spatial loss, spectral loss, and an additional loss component to enhance performance. The proposed methodology’s effectiveness was validated through experiments on WorldView-2 satellite images, IKONOS, and QuickBird, benchmarked against current state-of-the-art techniques. Experimental findings reveal that TAMINet significantly elevates the pan-sharpening performance for large-scale images, underscoring its potential to enhance multi-spectral remote sensing image quality. Full article

Despite advancements in remote sensing applications for grassland management, studies following the 2011 Fukushima Daiichi nuclear disaster have often been constrained by limited satellite imagery with insufficient focus on pasture changes. Utilizing different resolutions of optical satellite data is essential for monitoring spatiotemporal changes in grasslands. High resolutions provide detailed spatial information, whereas medium-resolution satellites offer an increased frequency and wider availability over time. This study had two objectives. First, we investigated the temporal changes in a mountainous pasture in Japan from 2007 to 2022 using high-resolution data from QuickBird, WorldView-2, and SPOT-6/7, along with readily available medium-resolution data from Sentinel-2 and Landsat-5/7/8. Second, we assessed the efficacy of different satellite image resolutions in capturing these changes. Grazing ceased in the target area after the 2011 Fukushima Daiichi nuclear accident owing to radiation. We categorized the images as grasses, broadleaf trees, and conifers. The results showed a 36% decline using high-resolution satellite image analysis and 35% using Landsat image analysis in the unused pasture area since grazing suspension in 2011, transitioning primarily to broadleaf trees, and relative stabilization by 2018. Tree encroachment was prominent at the eastern site, which has a lower elevation and steeper slope facing north, east, and south. WorldView-2 consistently outperformed Landsat-8 in accuracy. Landsat-8’s classification variation impedes its ability to capture subtle distinctions, particularly in zones with overlapping or neighboring land covers. However, Landsat effectively detected area reductions, similar to high-resolution satellites. Combining high- and medium-resolution satellite data leverages their respective strengths, compensates for their individual limitations, and provides a holistic perspective for analysis and decision-making. Full article