Detecting small objects in remote sensing images is a challenging task. Existing object detectors for remote sensing images suffer from two issues: (1) insufficient feature extraction for small objects in the backbone network and (2) feature misalignment and information loss for small objects in the neck network, leading to poor detection performance on small objects. To address these challenges, a small object detector named CABDet for remote sensing images that combines context and attention mechanisms is proposed. Specifically, an enhanced ResNet50 is designed as a novel backbone network that adaptively adjusts the size of receptive fields to fully extract feature information of small objects. Additionally, an adaptive multiscale feature pyramid network (AM-FPN) is proposed. To alleviate the problem of feature misalignment for small objects, AM-FPN leverages self-attention mechanisms to establish semantic and spatial dependencies between adjacent feature layers. Then to mitigate the issue of information loss for small objects, AM-FPN captures semantic dependencies between subregions of current layer features through self-attention mechanisms to preserve channel information. Extensive experiments were conducted on two demanding remote sensing datasets, namely dataset for object detection in aerial images and UCAS-high resolution aerial object detection dataset, to demonstrate the effectiveness of the proposed methodology in achieving superior detection performance when compared with contemporary state-of-the-art approaches.

Compared with the image captured in the natural scene, the image obtained by unmanned aerial vehicle (UAV) aerial photography has a more complex background and many dense small targets, which puts forward higher requirements for the detection accuracy of the target detection algorithm. However, because the UAV is a kind of small mobile device, how to ensure its real-time detection effect has been a problem. Aiming at these problems, the lightweight YOLOv7 algorithm, namely LRT-YOLOv7, is designed. First, the enhance feature fusion module and the transformer efficient layer aggregation networks module are proposed to improve the performance of feature extraction and fusion to enhance the efficiency of small target detection. Second, aiming at the problems of small target size and complex background in the UAV images, the detection head structure is redesigned in the YOLOv7-tiny algorithm to enhance the multi-scale feature fusion ability of the algorithm and thereby improve the algorithm’s detection accuracy for small targets. Finally, ablation, comparison, and visualization validation experiments were conducted using precision, recall, mean average precision, and frames per second (FPS) as evaluation indicators. The results show that the detection speed of the LRT-YOLOv7 algorithm on the self-made traffic target dataset is 133.8 FPS, and the precision indicator is 84.58%. Therefore, the LRT-YOLOv7 algorithm has high accuracy and real-time performance in traffic target detection tasks for UAV aerial imagery.

The advancement of near-ground remote sensing and artificial intelligence techniques has revolutionized field surveys, replacing traditional manual methods. Nevertheless, understanding and exploring the growth patterns and intricate morphology of natural elm tree crowns present significant challenges, especially when attempting to extract their features, which are often susceptible to interference from surrounding grass and vegetation. In addition, existing detection and segmentation models based on convolutional neural networks exhibit redundancies in their network architectures and employ less efficient algorithms, such as mask region-based convolutional neural networks. As a result, these models may not be the most suitable options for analyzing extensive and highly detailed remote-sensing image data. We focus on detecting trees in semi-arid regions and extracting their canopy parameters, such as canopy width and area. A training set is established by outlining a total of 20,594 tree canopies on high-spatial resolution unmanned aerial vehicle images. A two-stage instance segmentation model is proposed to develop a method for individual tree detection and efficient extraction of canopy parameters in complex natural environments. The results demonstrate the method’s capability to accurately detect the location, number, and canopy parameters (e.g., crown width and area) of individual trees in diverse natural scenes. The model achieves a detection speed of 13.3 fps@1024, with the model weight parameters totaling 8.08 M and computation requiring 8.96 Giga floating point operations per seconds (GFLOPs). Moreover, the detection accuracy and segmentation accuracy of individual trees on the validation set are reported as 0.463 and 0.465, respectively. Compared with Mack RCNN and Mask Scoring RCNN, the proposed method reduces the weight parameters and computational complexity of the model by 82.4%, 83.5% and 96.8%, 92.8%, respectively, while increasing the inference speed by 47.4% and 26.3%. This method offers an efficient and accurate solution for obtaining the structural parameters of individual trees.

Modifications in submerged aquatic vegetation (SAV) spatial and temporal abundance patterns indicate changes in marine environmental conditions or physical disturbances and need to be monitored. Vegetation percent cover (%cover) is recognized as one of the key parameters in SAV monitoring. Coastal waters of the Baltic Sea are often turbid and contain high amount of colored dissolved organic matter. These factors significantly reduce the water depth, where benthic parameters can be detected by remote sensing. Field campaigns were carried out in a low-transparency Pärnu Bay area to assess to what extent multispectral Sentinel-2 (S2) satellite can be used for SAV %cover mapping in such waters. An average depth restriction for S2 benthic vegetation detection remained near 1.5 to 2.0 m. Empirical and physics-based methods were applied to S2 imagery to compare their performance for SAV %cover retrieval. Both methods identified similar %cover patterns. Model validation results showed that R2 of the best-performing models remained between 0.56 and 0.66 and root-mean-square error between 22.11 and 28.06. As physics-based inversion models do not require extensive set of training data for model calibration, those can be used for retrospective time series analysis across multitemporal images.

Most health studies have used residential addresses to assess personal exposure to air pollution. These exposure assessments may suffer from bias due to not considering individual movement. Here, we collected 45,600 hourly movement trajectory data points for 185 individuals in Nanjing from COVID-19 epidemiological surveys. We developed a fusion algorithm to produce hourly 1-km PM2.5 concentrations, with a good performance for out-of-station cross validation (correlation coefficient of 0.89, root-mean-square error of 5.60 μg / m3, and mean absolute error (MAE) of 4.04 μg / m3). Based on these PM2.5 concentrations and location data, PM2.5 exposures considering individual movement were calculated and further compared with residence-based exposures. Our results showed that daily residence-based exposures had an MAE of 0.19 μg / m3 and were underestimated by <1 % overall. For hourly residence-based exposures, the MAE exhibited a diurnal variation: it decreased from 0.58 μg / m3 at 09:00 to 0.44 μg / m3 at 12:00 and then continuously increased to 0.74 μg / m3 at 17:00. The biases also depended on activity types and distances from home to activity locations. Specifically, the largest MAE (3.86 μg / m3) occurred in visits that were among the top four types of activity other than being at home. As distances changed from <10 to >30 km, the degree of underestimation for hourly residence-based exposures increased from 1% to 6%. This trend was more obvious for work activities, suggesting that personal exposure assessments should consider individual movement for work cases with long commuting distances.

The aim of this research is to analyze the influence of park design and location on their cooling effect during summer daytime in Barcelona. Spatial analytical methods, utilizing the land surface temperature data from the Landsat 8 satellite, were employed to assess the intensity and spatial extent of the Park Cool Island (PCI) in 86 parks over four consecutive years. The study investigated the influence of factors such as the proportion of green land cover, park shape, and surrounding characteristics on these PCI indicators using bivariate correlation and multiple linear regression analysis. Results revealed that 84 parks exhibited a positive cooling effect on their urban surroundings during the studied years, whereas the remaining parks showed no PCI. The statistical analysis indicated that these results are associated with the microclimatic uncertainty caused by the diverse relationships between the proportion of natural vegetated or pervious surfaces and the built spaces within the parks, proposed by the administration aiming to accommodate civic, cultural, and recreational usage. In conclusion, we discuss the parameters for modeling the interaction between park design and their urban surroundings in the context of climatic considerations. This information is crucial for leveraging urban parks as spaces that can mitigate the urban heat island effect while preserving their social value as community public spaces.

National Oceanic and Atmospheric Administration (NOAA)-20 (formerly Joint Polar Satellite System-1) is a polar-orbiting weather satellite launched on November 18, 2017. Visible-Infrared Imaging Radiometer Suite (VIIRS) is one of five instruments onboard NOAA-20 (N20), and it jointed on orbit the previous VIIRS instrument operating on the Suomi NPP (SNPP) spacecraft since November 2011. During post-launch testing, the accuracy of VIIRS sensor data record (SDR) geolocation products was evaluated using a system called ground control point matching. The control points are based on 30-m Landsat images as "ground truth," and the matchups employ their cross-correlation with the 375-m VIIRS images in band I1. Initial N20 VIIRS geolocation errors shortly after launch exceeded 2 km and were somewhat larger than those previously observed for SNPP VIIRS. The optimization of processing parameters successfully reduced the geolocation uncertainty to less than 400 m (at nadir, 3-sigma), which is comparable to the current performance for the SNPP VIIRS SDR products, with the majority of the geolocation errors within 200 m. The improvement was achieved by adjusting only the instrument-to-spacecraft rotation angles (the "mounting matrix"). Although the uncertainty evaluation was based on global matchups collected over 12-month periods, the parameter optimization was based on a reduction of the geolocation angular errors by reprocessing VIIRS SDR and re-analyzing the global matchups from 16-day orbit repeat periods. Geolocation of the 750-m day/night band (DNB), which is not co-registered with the 375-m I-bands and the 750-m M-bands, was evaluated and optimized separately, using the correlation between DNB and the I1 band re-projected images. Initial N20 VIIRS DNB geolocation errors were even larger than those observed for the other bands, but they were also successfully reduced by adjusting DNB-specific processing parameters. The initial improvements in the N20 VIIRS geolocation accuracy were achieved in 2018, and the processing parameters were further refined after an optical model correction in 2021.

Deforestation is one of the contributing factors to climate change. Climate change has a serious impact on human life, and it occurs due to emission of greenhouse gases, such as carbon dioxide, to the atmosphere. It is important to know the causes of deforestation for mitigation efforts, but there is a lack of data-driven research studies to predict these deforestation drivers. In this work, we propose a contrastive learning architecture, called Multimodal SuperCon, for classifying drivers of deforestation in Indonesia using satellite images obtained from Landsat 8. Multimodal SuperCon is an architecture which combines contrastive learning and multimodal fusion to handle the available deforestation dataset. Our proposed model outperforms previous work on driver classification, giving a 7% improvement in accuracy in comparison to a state-of-the-art rotation equivariant model for the same task.

The traditional generative adversarial network (GAN) is widely used in the field of synthetic aperture radar (SAR) ground target image generation. However, GAN has the problem of unstable gradient update, which can easily cause the loss of image feature information, resulting in a low similarity between the generated image and the real image. To solve these problems, we propose an improved Wasserstein GAN with gradient penalty (IWGAN-GP), which introduces dense connection in the generator, integrates feature information at different levels to achieve feature reuse, and alleviates the gradient disappearance problem caused by deep networks. Moreover, by introducing the squeeze-and-excitation (SE) module into the densely connected network, on the basis of considering high-level semantic information and low-level geometric texture details, the optimal fusion weights of each channel can be automatically obtained to fully explore important target information in SAR images. IWGAN-GP alleviates the gradient disappearance caused by the depth of the network, strengthens feature propagation, and realizes feature reuse. It can automatically obtain the optimal fusion weight of each channel and improve the similarity between the generated image and the real image. The superiority of IWGAN-GP is verified on the datasets of MSTAR.

We are focused on improving the resolution of images of moving targets in Inverse Synthetic Aperture Radar (ISAR) imaging. This could be achieved by recovering the scattering points of a target that have stronger reflections than other target points, leading to increasing the higher Radar Cross Section (RCS) of a target. These points, however, are sparse and when the received data is incomplete, moving targets would not be properly recognizable in ISAR images. To increase the resolution in ISAR imaging, we propose the 2-Dimensional-Reweighted Trace Minimization (2D-RWTM) method to retrieve frequencies of sparse scattering points in both range and cross-range directions. This method is a gridless super-resolution method, which does not depend on fitting the scattering point on the grids, leading to less complexity compared to the other methods. Using computer simulations, the proposed 2D-RWTM is compared to the Atomic Norm Minimization (ANM) in terms of the Mean Squared Errors (MSE). The results show that using the proposed method, the scattering points of a target are successfully recovered. It is shown that by selecting different weighting matrices and scattering points adjacent to each other, the recovery in ISAR imaging is still successful.