For the protection, restoration, and sustainable management of wetland ecosystems, precision in extracting high-quality wetland land cover information is crucial. This study focused on the National Nature Reserve of Liaohe Estuary in Panjin City, Liaoning Province, China. To enhance the classification accuracy of wetland land covers exhibiting similar spectral characteristics and alleviate the occurrence of the ‘salt-and-pepper’ effect, where certain land parcels are erroneously classified into multiple categories by pixel-based methods, an approach integrating object-oriented techniques and temporal features was employed for precise wetland land cover classification. The analysis utilized multi-temporal Sentinel-2 multispectral images. Initially, the images underwent segmentation using the SNIC method to generate uniform polygons, effectively mitigating misclassification issues. Subsequently, texture, geometry, band reflectance, and spectral deviation features were extracted for each segmented object. A total of 57 features, including vegetation and moisture components, were integrated to construct temporal characteristics. By applying the Random Forest (RF) algorithm in combination with Extreme Randomized Trees (ERT), 18 significant features influencing wetland extraction were identified. These selected features were then utilized to train a Random Forest (RF) model for classifying wetland land cover in the study area. The findings revealed that the integrated object-oriented and temporal feature classification approach achieved an impressive overall accuracy of 95.52% and a Kappa coefficient of 0.95 for the Liaohe Estuary wetland region. The accuracy for various land cover types reached 0.87 for both user and producer accuracy. Compared to alternative machine learning algorithms such as SegUnet++, SVM, and RF, the proposed method demonstrated a performance increase of 16.35%, 14.06%, and 6.14%, respectively. The incorporation of temporal features notably reduced land cover misclassifications, resulting in a 6.14% increase in overall accuracy and a 0.07 improvement in the Kappa coefficient compared to a method lacking temporal features. Particularly for categories like canals, aquaculture, rivers, and reservoirs, producer accuracy improved by over 7.5% and user accuracy by more than 2.9%. The effectiveness of the object-oriented approach was evident in addressing the “salt-and-pepper” effect, showcasing a rise of 2.81% in overall accuracy and 0.03 in Kappa coefficient compared to an approach not utilizing object-oriented techniques. In summary, the proposed classification method, integrating object-oriented methods and temporal features, offers superior accuracy in fine wetland land cover classification and mapping.

Leaf spot disease significantly impacts peanut growth. Timely, effective, and accurate monitoring of leaf spot severity is crucial for high-yield and high-quality peanut production. Hyperspectral technology from unmanned aerial vehicles (UAVs) is widely employed for disease detection in agricultural fields, but the low spatial resolution of imagery affects accuracy. In this study, peanuts with varying levels of leaf spot disease were detected using hyperspectral images from UAVs. Spectral features of crops and backgrounds were extracted using simple linear iterative clustering (SLIC), the homogeneity index, and k-means clustering. Abundance estimation was conducted using fully constrained least squares based on a distance strategy (D-FCLS), and crop regions were extracted through threshold segmentation. Disease severity was determined based on the average spectral reflectance of crop regions, utilizing classifiers such as XGBoost, the MLP, and the GA-SVM. Results indicate that crop spectra extracted using the superpixel-based unmixing method effectively captured spectral variability, leading to more accurate disease detection. By optimizing threshold values, a better balance between completeness and the internal variability of crop regions was achieved, allowing for the precise extraction of crop regions. Compared to other unmixing methods and manual visual interpretation techniques, the proposed method achieved excellent results, with an overall accuracy of 89.08% and a Kappa coefficient of 85.42% for the GA-SVM classifier. This method provides an objective, efficient, and accurate solution for detecting peanut leaf spot disease, offering technical support for field management with promising practical applications.

In recent years, accurate field monitoring has been a research hotspot in the domains of aerial remote sensing and satellite remote sensing. In view of this, this study proposes an innovative cross-platform super-resolution reconstruction method for remote sensing images for the first time, aiming to make medium-resolution satellites capable of field-level detection through a super-resolution reconstruction technique. The progressive growing generative adversarial network (PGGAN) model, which has excellent high-resolution generation and style transfer capabilities, is combined with a deep residual network, forming the Res-PGGAN model for cross-platform super-resolution reconstruction. The Res-PGGAN architecture is similar to that of the PGGAN, but includes a deep residual module. The proposed Res-PGGAN model has two main benefits. First, the residual module facilitates the training of deep networks, as well as the extraction of deep features. Second, the PGGAN structure performs well in cross-platform sensor style transfer, allowing for cross-platform high-magnification super-resolution tasks to be performed well. A large pre-training dataset and real data are used to train the Res-PGGAN to improve the resolution of Sentinel-2’s 10 m resolution satellite images to 0.625 m. Three evaluation metrics, including the structural similarity index metric (SSIM), the peak signal-to-noise ratio (PSNR), and the universal quality index (UQI), are used to evaluate the high-magnification images obtained by the proposed method. The images generated by the proposed method are also compared with those obtained by the traditional bicubic method and two deep learning super-resolution reconstruction methods: the enhanced super-resolution generative adversarial network (ESRGAN) and the PGGAN. The results indicate that the proposed method outperforms all the comparison methods and demonstrates an acceptable performance regarding all three metrics (SSIM/PSNR/UQI: 0.9726/44.7971/0.0417), proving the feasibility of cross-platform super-resolution image recovery.

Introduction: Monitoring grazing intensity is crucial for maintaining ecological balance and promoting the sustainable management of sandy grasslands. Traditional ground surveys and single-source remote sensing often lack the spatial resolution, spectral richness, and robustness required to accurately characterize heterogeneous grazing impacts. Unmanned aerial vehicle (UAV)-based multi-source remote sensing provides fine-scale, repeatable observations that can overcome the limitations of traditional field surveys. Methods: Grazing experiments were conducted in the sandy grasslands of Inner Mongolia, China, using UAVs to capture visible and multispectral imagery across plots subjected to different grazing intensities. Spectral responses were analyzed using mean–variance statistics and Tukey’s multiple comparison tests. A series of novel spectral indices were constructed based on separability analysis and integrated with traditional vegetation indices to address the limited sensitivity of conventional indices and multi-index feature redundancy. An automatic incremental feature selection (AIFS) algorithm was developed to adaptively optimize the feature subset and enhance model robustness, with a support vector machine classifier, k-nearest neighbor, and random forest used for grazing intensity recognition. Results: Distinct spectral responses to grazing disturbance were observed: visible bands increased with grazing intensity due to enhanced soil background effects, while red-edge and near-infrared bands effectively captured reductions in chlorophyll content and canopy structure under moderate to severe grazing. Traditional vegetation indices were sensitive to extreme grazing, whereas the proposed indices showed superior performance in distinguishing moderate grazing levels. The AIFS-optimized feature subset reduced redundancy and improved model accuracy, achieving the highest recognition performance (OA=92.13%, Kappa=88.99%)—outperforming models using all features or single-source data. Discussion: Integrating UAV visible and multispectral imagery with intelligent feature selection enhances the detection of grazing-induced vegetation responses. This approach provides a robust framework for high-precision grassland monitoring and sustainable ecological management in arid and semi-arid regions.

Accurate segmentation of fruit tree leaf diseases is critical for yield protection and precision crop management, yet it is challenging due to complex field conditions, irregular leaf morphology, and diverse lesion patterns. To address these issues, Disease-Seg, a lightweight real-time segmentation framework, is proposed. It integrates CNN and Transformer with a parallel fusion architecture to capture local texture and global semantic context. The Extended Feature Module (EFM) enlarges the receptive field while retaining fine details. A Deep Multi-scale Attention mechanism (DM-Attention) allocates channel weights across scales to reduce redundancy, and a Feature-weighted Fusion Module (FWFM) optimizes integration of heterogeneous feature maps, enhancing multi-scale representation. Experiments show that Disease-Seg achieves 90.32% mIoU and 99.52% accuracy, outperforming representative CNN, Transformer, and hybrid-based methods. Compared with HRNetV2, it improves mIoU by 6.87% and FPS by 31, while using only 4.78 M parameters. It maintains 69 FPS on 512 × 512 crops and requires approximately 49 ms per image on edge devices, demonstrating strong deployment feasibility. On two grape leaf diseases from the PlantVillage dataset, it achieves 91.19% mIoU, confirming robust generalization. These results indicate that Disease-Seg provides an accurate, efficient, and practical solution for fruit leaf disease segmentation, enabling real-time monitoring and smart agriculture applications.

In recent years, super-resolution (SR) reconstruction techniques for remote sensing imagery has attracted extensive attention due to its advantages of a low cost and flexible and convenient application. Unlike conventional SR tasks, the SR reconstruction of remote sensing images under real-world conditions is usually accompanied by a large number of objective unfavorable factors, including high levels of noise, cloud occlusion, and variability across different sensors. Consequently, real-scene SR reconstruction constitutes a multitask problem, yet existing SR approaches seldom address these challenges in a unified framework. To overcome these limitations, this study proposes a multitask SR reconstruction network named multitask SR reconstruction generative adversarial network (GAN) specifically designed for real-world remote sensing imagery. The proposed network adopts the GAN model’s structure, and integrates three dedicated modules within the generator: a deep feature extraction module, a spectral transfer module, and a thin cloud removal module. Within this framework, we design a novel Swin Transformer module to enhance spatial diversity and training stability in image reconstruction. Moreover, we introduce a framework termed the efficient feature aggregation architecture, which ensures effective feature fusion while enabling seamless collaboration among these components. Together, these components enable effective adaptation to cross-sensor SR reconstruction tasks under complex real-world conditions. The proposed method is verified by both quantitative and qualitative analyses. The SR reconstruction results obtained on multiple datasets show that the proposed method is superior to the existing mainstream methods.