- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Bistatic SAR: Forecasting Spatial Resolution
摘要： This paper derives closed form expressions for bistatic synthetic aperture radar spatial resolution of a generalized system from the k-space or the wavenumber domain. These spatial resolution equations have not previously appeared in the literature. From these equations, significant resolution is found in both range and cross-range forecasting a forward-scatter bistatic synthetic aperture radar image when the elevation angles of each bistatic platform are significantly different. Simulation and lab tests demonstrated the forward scatter resolution.
关键词： image resolution,synthetic aperture radar,radar remote sensing,spatial resolution,radar imaging,bistatic radar,microwave imaging
更新于2025-09-23 15:22:29
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Detection of Multiclass Objects in Optical Remote Sensing Images
摘要： Object detection in complex optical remote sensing images is a challenging problem due to the wide variety of scales, densities, and shapes of object instances on the earth surface. In this letter, we focus on the wide-scale variation problem of multiclass object detection and propose an effective object detection framework in remote sensing images based on YOLOv2. To make the model adaptable to multiscale object detection, we design a network that concatenates feature maps from layers of different depths and adopt a feature introducing strategy based on oriented response dilated convolution. Through this strategy, the performance for small-scale object detection is improved without losing the performance for large-scale object detection. Compared to YOLOv2, the performance of the proposed framework tested in the DOTA (a large-scale data set for object detection in aerial images) data set improves by 4.4% mean average precision without adding extra parameters. The proposed framework achieves real-time detection for 1024 ×1024 image using Titan Xp GPU acceleration.
关键词： Feature introducing strategy,optical remote sensing image,object detection,oriented response (OR) dilated convolution
更新于2025-09-23 15:22:29
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Imaging-Based Nearshore Bathymetry Measurement Using an Unmanned Aircraft System
摘要： An imaging-based method to estimate the nearshore bathymetry in the surf zone is described. The method uses imagery collected by an unmanned aircraft system (UAS), or a consumer drone. The UAS was flown over the area of interest to record video, and a particle image velocimetry (PIV) technique was then applied to analyze the image frames to retrieve the wave celerity. Using the shallow water approximation to the linear-wave dispersion relation, wave celerity from the imagery could be used to deduce the local water depth. After combining the water depth inversion at multiple points from within the area of interest, the bathymetry was constructed. To validate the method, water depths from 25 spatial points were surveyed with a total station during a trial in the nearshore surf zone at Freeport, Texas. The root-mean-square error (RMSE) was estimated as 0.132 m. By minimizing the RMSE, the correction factor that accounts for the wave nonlinearity in estimating wave celerity was estimated as 1.02. This new and simple approach provides simultaneous measurement of bathymetry and surface velocity field mainly in the surf zone, where breaking/broken waves and energetic sediment transport frequently dominate, and does not require a high-end UAS, resulting in greater flexibility in sampling across space and time.
关键词： Particle image velocimetry,Remote sensing,Nearshore bathymetry,Unmanned aircraft system
更新于2025-09-23 15:22:29
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FVI—A Floating Vegetation Index Formed with Three Near-IR Channels in the 1.0–1.24 μm Spectral Range for the Detection of Vegetation Floating over Water Surfaces
摘要： Through the analysis of hyperspectral imaging data collected over water surfaces covered by floating vegetation, such as Sargassum and algae, we observed that the spectra commonly contain a reflectance peak centered near 1.07 μm. This peak results from the competing effects between the well-known vegetation reflectance plateau in the 0.81–1.3 μm spectral range and the absorption effects above 0.75 μm by liquid water within the vegetation and in the surrounding water bodies. In this article, we propose a new index, namely the floating vegetation index (FVI), for the hyperspectral remote sensing of vegetation over surface layers of oceans and inland lakes. In the formulation of the FVI, one channel centered near 1.0 μm and another 1.24 μm are used to form a linear baseline. The reflectance value of the third channel centered at the 1.07-μm reflectance peak above the baseline is defined as the FVI. Hyperspectral imaging data acquired with the AVIRIS (Airborne Visible Infrared Imaging Spectrometer) instrument over the Gulf of Mexico and over salt ponds near Moffett Field in southern portions of the San Francisco Bay were used to demonstrate the success in detecting Sargassum and floating algae with this index. It is expected that the use of this index for the global detection of floating vegetation from hyperspectral imaging data to be acquired with future satellite sensors will result in improved detection and therefore enhanced capability in estimating primary production, a measure of how much carbon is fixed per unit area per day by oceans and inland lakes.
关键词： Sargassum,sensors,remote sensing,hyperspectrum,vegetation index,algae
更新于2025-09-23 15:22:29
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Development of Raman Lidar for Remote Sensing of CO2 Leakage at an Artificial Carbon Capture and Storage Site
摘要： We developed a Raman lidar system that can remotely detect CO2 leakage and its volume mixing ratio (VMR). The system consists of a laser, a telescope, an optical receiver, and detectors. Indoor CO2 cell measurements show that the accuracy of the Raman lidar is 99.89%. Field measurements were carried out over a four-day period in November 2017 at the Eumsong Environmental Impact Evaluation Test Facility (EIT), Korea, where a CO2 leak was located 0.2 km from the Raman lidar. The results show good agreement between CO2 VMR measured by the Raman lidar system (CO2 VMRRaman LIDAR) and that measured by in situ instruments (CO2 VMRIn-situ). The correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and percentage difference between CO2 VMRIn-situ and CO2 VMRRaman LIDAR are 0.81, 0.27%, 0.37%, and 4.92%, respectively. The results indicate that Raman lidar is an effective tool in detecting CO2 leakage and in measuring CO2 VMR remotely.
关键词： CO2,CO2 leakage remote sensing,Carbon capture and storage,Raman lidar
更新于2025-09-23 15:22:29
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Identification of Wheat Yellow Rust Using Optimal Three-Band Spectral Indices in Different Growth Stages
摘要： Yellow rust, a widely known destructive wheat disease, affects wheat quality and causes large economic losses in wheat production. Hyperspectral remote sensing has shown potential for the detection of plant disease. This study aimed to analyze the spectral reflectance of the wheat canopy in the range of 350–1000 nm and to develop optimal spectral indices to detect yellow rust disease in wheat at different growth stages. The sensitive wavebands of healthy and infected wheat were located in the range 460–720 nm in the early-mid growth stage (from booting to anthesis), and in the ranges 568–709 nm and 725–1000 nm in the mid-late growth stage (from filling to milky ripeness), respectively. All possible three-band combinations over these sensitive wavebands were calculated as the forms of PRI (Photochemical Reflectance Index) and ARI (Anthocyanin Reflectance Index) at different growth stages and assessed to determine whether they could be used for estimating the severity of yellow rust disease. The optimal spectral index for estimating wheat infected by yellow rust disease was PRI (570, 525, 705) during the early-mid growth stage with R2 of 0.669, and ARI (860, 790, 750) during the mid-late growth stage with R2 of 0.888. Comparison of the proposed spectral indices with previously reported vegetation indices were able to satisfactorily discriminate wheat yellow rust. The classification accuracy for PRI (570, 525, 705) was 80.6% and the kappa coefficient was 0.61 in early-mid growth stage, and the classification accuracy for ARI (860, 790, 750) was 91.9% and the kappa coefficient was 0.75 in mid-late growth stage. The classification accuracy of the two indices reached 84.1% and 93.2% in the early-mid and mid-late growth stages in the validated dataset, respectively. We conclude that the three-band spectral indices PRI (570, 525, 705) and ARI (860, 790, 750) are optimal for monitoring yellow rust infection in these two growth stages, respectively. Our method is expected to provide a technical basis for wheat disease detection and prevention in the early-mid growth stage, and the estimation of yield losses in the mid-late growth stage.
关键词： yellow rust disease,three-band spectral index,different growth stages,hyperspectral remote sensing,wheat infection
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 16th International Conference on Embedded and Ubiquitous Computing (EUC) - Bucharest, Romania (2018.10.29-2018.10.31)] 2018 IEEE 16th International Conference on Embedded and Ubiquitous Computing (EUC) - Remote Sensing Computing Model for Forest Monitoring in Cloud
摘要： Natural resource management deals with managing the way in which people and natural landscapes interact. Recent years have made significant progress in developing and improving technology that provides multiple ways of monitoring natural resources. This paper propose a remote sensing computing model in Cloud environment in order to monitor forests. We tackle the question of creating a monitoring system, using satellites images of forest areas. Furthermore, the paper analyses the performance of current technologies for storage management, automation deployment, system scaling, integration with a public or private cloud. Also, we take into consideration the possibility of parallelizing current algorithms for image processing.
关键词： Remote Sensing Computing,Forest Monitoring,Cloud Computing,Microservices
更新于2025-09-23 15:22:29
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The AOTF-based NO<sub>2</sub> camera
摘要： The abundance of NO2 in the boundary layer relates to air quality and pollution source monitoring. Observing the spatiotemporal distribution of NO2 above well-delimited (flue gas stacks, volcanoes, ships) or more extended sources (cities) allows for applications such as monitoring emission fluxes or studying the plume dynamic chemistry and its transport. So far, most attempts to map the NO2 field from the ground have been made with visible-light scanning grating spectrometers. Benefiting from a high retrieval accuracy, they only achieve a relatively low spatiotemporal resolution that hampers the detection of dynamic features. We present a new type of passive remote sensing instrument aiming at the measurement of the 2-D distributions of NO2 slant column densities (SCDs) with a high spatiotemporal resolution. The measurement principle has strong similarities with the popular filter-based SO2 camera as it relies on spectral images taken at wavelengths where the molecule absorption cross section is different. Contrary to the SO2 camera, the spectral selection is performed by an acousto-optical tunable filter (AOTF) capable of resolving the target molecule's spectral features. The NO2 camera capabilities are demonstrated by imaging the NO2 abundance in the plume of a coal-fired power plant. During this experiment, the 2-D distribution of the NO2 SCD was retrieved with a temporal resolution of 3 min and a spatial sampling of 50 cm (over a 250 × 250 m2 area). The detection limit was close to 5 × 1016 molecules cm?2, with a maximum detected SCD of 4 × 1017 molecules cm?2. Illustrating the added value of the NO2 camera measurements, the data reveal the dynamics of the NO to NO2 conversion in the early plume with an unprecedent resolution: from its release in the air, and for 100 m upwards, the observed NO2 plume concentration increased at a rate of 0.75–1.25 g s?1. In joint campaigns with SO2 cameras, the NO2 camera could also help in removing the bias introduced by the NO2 interference with the SO2 spectrum.
关键词： NO2,AOTF,plume,remote sensing,air quality,camera,acousto-optical tunable filter
更新于2025-09-23 15:22:29
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Deep Distillation Recursive Network for Remote Sensing Imagery Super-Resolution
摘要： Deep convolutional neural networks (CNNs) have been widely used and achieved state-of-the-art performance in many image or video processing and analysis tasks. In particular, for image super-resolution (SR) processing, previous CNN-based methods have led to significant improvements, when compared with shallow learning-based methods. However, previous CNN-based algorithms with simple direct or skip connections are of poor performance when applied to remote sensing satellite images SR. In this study, a simple but effective CNN framework, namely deep distillation recursive network (DDRN), is presented for video satellite image SR. DDRN includes a group of ultra-dense residual blocks (UDB), a multi-scale purification unit (MSPU), and a reconstruction module. In particular, through the addition of rich interactive links in and between multiple-path units in each UDB, features extracted from multiple parallel convolution layers can be shared effectively. Compared with classical dense-connection-based models, DDRN possesses the following main properties. (1) DDRN contains more linking nodes with the same convolution layers. (2) A distillation and compensation mechanism, which performs feature distillation and compensation in different stages of the network, is also constructed. In particular, the high-frequency components lost during information propagation can be compensated in MSPU. (3) The final SR image can benefit from the feature maps extracted from UDB and the compensated components obtained from MSPU. Experiments on Kaggle Open Source Dataset and Jilin-1 video satellite images illustrate that DDRN outperforms the conventional CNN-based baselines and some state-of-the-art feature extraction approaches.
关键词： feature distillation,compensation unit,ultra-dense connection,super-resolution,video satellite,remote sensing imagery
更新于2025-09-23 15:22:29
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Mixed Pixel Decomposition Based on Extended Fuzzy Clustering for Single Spectral Value Remote Sensing Images
摘要： The presence of mixed pixels in remote sensing images is the major issue for accurate classification. In this paper, we have focused on two aspects of mixed pixel problem: firstly, to identify mixed pixels from an image and secondly to label them to their appropriate class. In phase I, extraction of mixed pixels has been performed from the RSI images-based super-pixel algorithm and RGB model by using fuzzy C-means (FCM). In phase II, the extracted mixed pixel from phase I has been decomposed to the appropriate class. This new proposed technique is the amalgamation of PSO-FCM (particle swarm optimization-fuzzy C-means) for clustering of mixed pixels and ANN-BPO (artificial neural network-biogeography-based particle swarm optimization) for the classification purpose. Experimental results reveal that the proposed method has improved the accuracy as compared to the existing techniques and succeeds in better classification of the remote sensing images.
关键词： Fuzzy C-means,BBO,Remote sensing images,Pure pixels,Mixed pixels,PSO,Neural network
更新于2025-09-23 15:22:29