Resolution Aerial Images Research Articles

Estimating water surface elevation for a wetland using integrated multi-sourced remote sensing data

Surface water plays an important role in understanding the hydrological behaviour of a wetland and is crucial for the sustainability of wetland ecosystems. Remote sensing increasingly is used for the estimation of surface water levels in larger inland waterbodies. However, there are few investigations that have employed multi-sourced remote sensing data for water level predictions in wetlands, which was the motivation for undertaking this study. Sentinel-2 and Landsat-8 are among the latest satellites providing optical imagery with high spatial resolution and coverage that are available in the public domain. Different water indices have been applied to estimate surface water levels using these satellite image sources; however, what index to use for a particular application requires thorough, site-specific analysis. In this study, the Normalized Difference Water Index (NDWI), two versions of the Modified Normalized Difference Water Index (MNDWI), and the Water Ratio Index (WRI) were used to estimate water levels in a constructed wetland, as part of an effort to better guide regulation and decision-making for a local management agency. The satellite data were complemented with high resolution aerial photogrammetric images and LiDAR data to assess the accuracy of water level predictions provided by the satellite images. The photogrammetric images were used as reference datasets while the LiDAR data supported the development of area-elevation curves for the wetland. Accuracy assessment between the satellite and reference images was performed using the Kappa co-efficient (K). MNDWI performed better than the other water indices for both satellite data sources; however, the optimum threshold was different for each satellite (− 0.35 for Sentinel-2 and − 0.25 for Landsat-8). K values for the optimum threshold ranged between 0.72 and 0.77 for Sentinel-2 and 0.73 and 0.87 for Landsat-8. The water levels estimated using the remotely sensed data were assessed against in situ, continuously measured water levels using multiple efficiency evaluation metrics including R2, RMSE, and SSE. Estimated water levels with Sentinel-2 and Landsat-8 resulted in an R2 of 0.86 and 0.88, RMSE of 0.04 m and 0.06 m, and an SSE of 0.02 m and 0.06 m, respectively. These results show that even for a small wetland, it is possible to use satellite imagery to estimate water levels with high accuracy.

Very high resolution aerial image orthomosaics, point clouds, and elevation datasets of select permafrost landscapes in Alaska and northwestern Canada

Abstract. Permafrost landscapes in the Arctic are highly vulnerable to warming, with rapid changes underway. High-resolution remote sensing, especially aerial datasets, offers valuable insights into current permafrost characteristics and thaw dynamics. Here, we present a new dataset of very high resolution orthomosaics, point clouds, and digital surface models that we acquired over permafrost landscapes in northwestern Canada and northern and northwestern Alaska for the purpose of better understanding the impacts of climate change on permafrost landscapes. The imagery was collected with the Modular Aerial Camera System (MACS) during aerial campaigns conducted by the Alfred Wegener Institute in the summers of 2018, 2019, and 2021. The MACS was specifically developed by the German Aerospace Center (DLR) for operation under challenging light conditions in polar environments. It features cameras in the optical and the near-infrared wavelengths with up to a 16 MP resolution. We processed the images to four-band (blue–green–red–near-infrared) orthomosaics and digital surface models with spatial resolutions of 7 to 20 cm as well as 3D point clouds with point densities of up to 41 points m−2. The dataset collection features 102 subprojects from 35 target regions (1.4–161.1 km2 in size). Project sizes range from 4.8 to 336 GB. In total, 3.17 TB were published. The horizontal precision of the datasets is in the range of 1–2 px and vertical precision is better than 0.10 m. The datasets are not radiometrically calibrated. Overall, these very high resolution images and point clouds provide significant opportunities for mapping permafrost landforms and generating detailed training datasets for machine learning, can serve as a baseline for change detection for thermokarst and thermo-erosion processes, and help with upscaling of field measurements to lower-resolution satellite observations. The dataset is available on the PANGAEA repository at https://doi.org/10.1594/PANGAEA.961577 (Rettelbach et al., 2024).

Accuracy Enhancement of Orthoimage Generation for Urban Areas

This research addresses the problems of conventional orthoimages created from aerial imagery of urban areas, especially relief displacements, occlusions and information loss caused by man-made objects. An approach is presented to generate more accurate orthoimages based on elevation models that exactly describe the entire surface of the observed region. The approach utilizes the digital building model, in addition to the digital terrain model, in the orthorectification process. Occluded areas are identified and filled in the final orthoimage by merging data from conjugate images. The data used in the research consists of a small block of high resolution aerial images covering an urban area. The images are captured recently with the digital aerial camera UltraCam-D. The image pixel size is 9 m by 9 m yielding a nearly 10-cm ground sample distance. The results reached by applying the proposed approach on the test images have been promising. They have proven the validity of the approach in generating more proper orthoimages.

A High Spatial Resolution Aerial Image Dataset and an Efficient Scene Classification Model

A High Spatial Resolution Aerial Image Dataset and an Efficient Scene Classification Model

Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images

The composition and structure of mountain vegetation are complex and changeable, and thus urgently require the integration of Object-Based Image Analysis (OBIA) and Deep Convolutional Neural Networks (DCNNs). However, while integration technology studies are continuing to increase, there have been few studies that have carried out the classification of mountain vegetation by combining OBIA and DCNNs, for it is difficult to obtain enough samples to trigger the potential of DCNNs for mountain vegetation type classification, especially using high-spatial-resolution remote sensing images. To address this issue, we propose a self-adaptive-filling method (SAF) to incorporate the OBIA method to improve the performance of DCNNs in mountain vegetation type classification using high-spatial-resolution aerial images. Using this method, SAF technology was employed to produce enough regular sample data for DCNNs by filling the irregular objects created by image segmenting using interior adaptive pixel blocks. Meanwhile, non-sample segmented image objects were shaped into different regular rectangular blocks via SAF. Then, the classification result was defined by voting combining the DCNN performance. Compared to traditional OBIA methods, SAF generates more samples for the DCNN and fully utilizes every single pixel of the DCNN input. We design experiments to compare them with traditional OBIA and semantic segmentation methods, such as U-net, MACU-net, and SegNeXt. The results show that our SAF-DCNN outperforms traditional OBIA in terms of accuracy and it is similar to the accuracy of the best performing method in semantic segmentation. However, it reduces the common pretzel phenomenon of semantic segmentation (black and white noise generated in classification). Overall, the SAF-based OBIA using DCNNs, which is proposed in this paper, is superior to other commonly used methods for vegetation classification in mountainous areas.

FUSION OF DIRECT GEOREFERENCED AERIAL DRONE WITH TERRESTRIAL LASER SCANNER DATA THE CASE OF THE ROMAN BATHS OF AMATHUS, CYPRUS

Abstract. The fusion of geomatic techniques with different accuracy and resolution has been used in recent years to applications of geometrical documentation for several archaeological sites. In this research article, the case of the Roman Baths of Amathus in Limassol, Cyprus was investigated.Aerial photogrammetry and Terrestrial Laser Scanning (TLS) techniques were used to produce the final product. The photogrammetric data were based on Post-Processed Kinematic (PPK) imagery while the TLS georeference was based on targets.The main research objective of this paper is to examine the reliability of PPK photogrammetric data without the use of Ground Control Points (GCPs) and how well they can be integrated with TLS data. Also, the two acquisition techniques were compared and indicated that 0.005 m Ground Sampling Distance (GSD) resolution aerial images and 0.0061/10 m resolution scans can be qualitatively fused.The research paper will present the entire methodology up to the generation of the final 3D photorealistic product.

Solving photogrammetric cold cases using AI-based image matching: New potential for monitoring the past with historical aerial images

Solving photogrammetric cold cases using AI-based image matching: New potential for monitoring the past with historical aerial images

Improving the performance of learned descriptors in the matching of high spatial resolution aerial images by proposing a large-scale dataset of vertical images

Improving the performance of learned descriptors in the matching of high spatial resolution aerial images by proposing a large-scale dataset of vertical images

Spatiotemporal changes of coastal land use land cover and its drivers in Shanghai, China between 1989 and 2015

Spatiotemporal changes of coastal land use land cover and its drivers in Shanghai, China between 1989 and 2015

Comparing hydrologic impacts on recreational trails to remotely sensed data

Comparing hydrologic impacts on recreational trails to remotely sensed data

Multi-Scale and Context-Aware Framework for Flood Segmentation in Post-Disaster High Resolution Aerial Images

Floods are the most frequent natural disasters, occurring almost every year around the globe. To mitigate the damage caused by a flood, it is important to timely assess the magnitude of the damage and efficiently conduct rescue operations, deploy security personnel and allocate resources to the affected areas. To efficiently respond to the natural disaster, it is very crucial to swiftly obtain accurate information, which is hard to obtain during a post-flood crisis. Generally, high resolution satellite images are predominantly used to obtain post-disaster information. Recently, deep learning models have achieved superior performance in extracting high-level semantic information from satellite images. However, due to the loss of multi-scale and global contextual features, existing deep learning models still face challenges in extracting complete and uninterrupted results. In this work, we proposed a novel deep learning semantic segmentation model that reduces the loss of multi-scale features and enhances global context awareness. Generally, the proposed framework consists of three modules, encoder, decoder and bridge, combined in a popular U-shaped scheme. The encoder and decoder modules of the framework introduce Res-inception units to obtain reliable multi-scale features and employ a bridge module (between the encoder and decoder) to capture global context. To demonstrate the effectiveness of the proposed framework, we perform an evaluation using a publicly available challenging dataset, FloodNet. Furthermore, we compare the performance of the proposed framework with other reference methods. We compare the proposed framework with recent reference models. Quantitative and qualitative results show that the proposed framework outperforms other reference models by an obvious margin.

Sci-Net: scale-invariant model for buildings segmentation from aerial imagery

Buildings’ segmentation is a fundamental task in the field of earth observation and aerial imagery analysis. Most existing deep learning-based methods in the literature can be applied to a fixed or narrow-range spatial resolution imagery. In practical scenarios, users deal with a broad spectrum of image resolutions. Thus, a given aerial image often needs to be re-sampled to match the spatial resolution of the dataset used to train the deep learning model, which results in a degradation in segmentation performance. To overcome this challenge, we propose, in this manuscript, scale-invariant neural network (Sci-Net) architecture that segments buildings from wide-range spatial resolution aerial images. Specifically, our approach leverages UNet hierarchical representation and dense atrous spatial pyramid pooling to extract fine-grained multi-scale representations. Sci-Net significantly outperforms state-of-the-art models on the open cities AI and the multi-scale building datasets with a steady improvement margin across different spatial resolutions.

Space-to-speed architecture supporting acceleration on VHR image processing

Space-to-speed architecture supporting acceleration on VHR image processing

SWDet: Anchor-Based Object Detector for Solid Waste Detection in Aerial Images

As we all know, waste pollution is one of the most serious environmental issues in the world. Efficient detection of solid waste (SW) in aerial images can improve subsequent waste classification and automatic sorting on the ground. However, traditional methods have some problems, such as poor generalization and limited detection performance. This article presents an anchor-based object detector for solid waste in aerial images (SWDet). Specifically, we construct asymmetric deep aggregation (ADA) network with structurally reparameterized asymmetric blocks to extract waste features with inconspicuous appearance. Besides, considering the waste with blurred boundaries caused by the resolution of aerial images, this article constructs efficient attention fusion pyramid network (EAFPN) to obtain contextual information and multiscale geospatial information via attention fusion. And the model can capture the scattering features of irregular shape waste. In addition, we construct the dataset for solid waste aerial detection (SWAD) by collecting aerial images of SW in Henan Province, China, to validate the effectiveness of our method. Experimental results show that SWDet outperforms most of existing methods for SW detection in aerial images.

Analysis of superelevation and debris flow velocities at Illgraben, Switzerland

The forced vortex equation, based on the cross-stream inclination of a flow surface as it passes through a bend, is a common approach to estimating debris flow velocities. Here, we present the preliminary results of a study of superelevation and the correction factor k, used to adapt the forced vortex equation to debris flows, based on data from the Illgraben torrent in Switzerland. The definition of the radius of curvature, a factor in the calculation of superelevation velocities, is not found to exercise a large influence on the calculated velocities when using high resolution aerial images, with the choice of cross-section location and k-factor exercising a more significant influence. The k-factors found here fall within the range previously reported in the literature, ranging from approximately 1 to 7, and a previously suggested non-linear relationship with Froude numbers is evident in the dataset. Following the debris flow season of 2022, the study will be continued with additional debris flow events and the investigative methods will be extended to include high-resolution LiDAR sensors installed along the Illgraben torrent.

Towards Large-Scale Small Object Detection: Survey and Benchmarks.

With the rise of deep convolutional neural networks, object detection has achieved prominent advances in past years. However, such prosperity could not camouflage the unsatisfactory situation of Small Object Detection (SOD), one of the notoriously challenging tasks in computer vision, owing to the poor visual appearance and noisy representation caused by the intrinsic structure of small targets. In addition, large-scale dataset for benchmarking small object detection methods remains a bottleneck. In this paper, we first conduct a thorough review of small object detection. Then, to catalyze the development of SOD, we construct two large-scale Small Object Detection dAtasets (SODA), SODA-D and SODA-A, which focus on the Driving and Aerial scenarios respectively. SODA-D includes 24828 high-quality traffic images and 278433 instances of nine categories. For SODA-A, we harvest 2513 high resolution aerial images and annotate 872069 instances over nine classes. The proposed datasets, as we know, are the first-ever attempt to large-scale benchmarks with a vast collection of exhaustively annotated instances tailored for multi-category SOD. Finally, we evaluate the performance of mainstream methods on SODA. We expect the released benchmarks could facilitate the development of SOD and spawn more breakthroughs in this field.

Super-resolution with generative adversarial networks for improved object detection in aerial images

PurposeData quality and data resolution are essential for computer vision tasks like medical image processing, object detection, pattern recognition and so on. Super-resolution is a way to increase the image resolution, and super-resolved images contain more information compared to their low-resolution counterparts. The purpose of this study is analyzing the effects of the super resolution models trained before on object detection for aerial images.Design/methodology/approachTwo different models were trained using the Super-Resolution Generative Adversarial Network (SRGAN) architecture on two aerial image data sets, the xView and the Dataset for Object deTection in Aerial images (DOTA). This study uses these models to increase the resolution of aerial images for improving object detection performance. This study analyzes the effects of the model with the best perceptual index (PI) and the model with the best RMSE on object detection in detail.FindingsSuper-resolution increases the object detection quality as expected. But, the super-resolution model with better perceptual quality achieves lower mean average precision results compared to the model with better RMSE. It means that the model with a better PI is more meaningful to human perception but less meaningful to computer vision.Originality/valueThe contributions of the authors to the literature are threefold. First, they do a wide analysis of SRGAN results for aerial image super-resolution on the task of object detection. Second, they compare super-resolution models with best PI and best RMSE to showcase the differences on object detection performance as a downstream task first time in the literature. Finally, they use a transfer learning approach for super-resolution to improve the performance of object detection.

Effects of local land use on riparian vegetation, water quality, and in situ toxicity

The conversion of riparian forests into agricultural land results in the loss of water quality and aquatic biota health. The objectives of this study were therefore to determine the proportion of land use with emphasis on the type of vegetation cover; evaluate the limnological parameters and concentration of inorganic elements in the water of the São José stream in the Dourados River Basin, Mato Grosso do Sul, Brazil; correlate land use with the concentrations of inorganic elements in water; and evaluate the risk to preservation aquatic biota and in situ toxic effect on Astyanax lacustris. We collected samples from the São José Stream in 2020 and evaluated land use with high resolution aerial images. The inorganic elements in water samples were quantified using inductively coupled plasma optical emission spectrometry. In the surroundings of the São José stream, a reduction in riparian forest was observed with a proportion of only 16.32% of the area and the predominance of agricultural areas with 75.06%. The concentrations of dissolved oxygen (1.510 mg L-1) and P (> 0.235 mg L-1) in the water did not comply with the national legislation. In addition, Al and P indicated risks regarding the conservation of aquatic biota (risk quotient >1). The in situ evaluation of A. lacustris also revealed toxicity in the water. The results indicate environmental imbalance in the São José stream, requiring mitigation measures for its restoration and the sustainable use of its resources.
 Keywords: anthropic action, inorganic contaminants, risks for aquatic biota.

Advanced Landslide Prediction Utilising Unmanned Aerial Vehicle (UAV)

Slope stability involves developing a terrain slope profile and determining the geometry of the failure surface to compute the slope factor of safety. Terrain capture and reconstruction traditionally has been expensive, time consuming and manpower intensive. This innovative method utilizing unmanned aerial system technology alongside the advancement in graphics processing can be proven as more effective and cost efficient. This research aims to develop a method to forecast the occurrence of landslide by utilizing drone technology. The objectives are to obtain data on terrain slope, create 3D model of the land, then perform analysis on the slope stability. Drone technology equipped with high accuracy GPS system captures high resolution aerial images of a land area for mapping. The data obtained is uploaded into computer software to generate the 3D model of the land. Then, the critical details such as contours or the elevations can be extracted. This information is transferred into an alternative software such as GeoStudio to analyze the stability of slope. Thus, the results can be used to predict the level of safety of the terrain slope and taken for further safety measures involving the particular area.

Optimal and Fully Connected Deep Neural Networks Based Classification Model for Unmanned Aerial Vehicle Using Hyperspectral Remote Sensing Images

Unmanned Aerial Vehicle (UAV) is treated as an effective technique for gathering high resolution aerial images. The UAV based aerial image collection is highly preferred due to its inexpensive and effective nature. However, automatic classification of aerial images poses a major challenging issue in the design of UAV, which could be handled by the deep learning (DL) models. This study designs a novel UAV assisted DL based image classification model (UAVDL-ICM) for Industry 4.0 environment. The proposed UAVDL-ICM technique involves an ensemble of voting based three DL models, namely Residual network (ResNet), Inception with ResNetv2, and Densely Connected Networks (DenseNet). Also, the hyperparameter tuning of these DL models takes place using a genetic programming (GP) approach. Finally, Oppositional Water Wave Optimization (OWWO) with Fully Connected Deep Neural networks (FCDNN) is employed for the classification of aerial images. A wide range of simulations takes place and the results are examined in terms of different parameters. A detailed comparative study highlighted the betterment of the UAVDL-ICM technique compared to other recent approaches.