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Found 34 papers, 18 papers with code
A Depth-Adaptive Filtering Method for Effective GPR Tree Roots Detection in Tropical Area
The technique is successfully validated using SFCW GPR data from actual sites in a tropical area with different soil moisture levels, and the two-dimensional (2D) radar map of subsurface root systems is highly improved compared to existing methods.
3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion
The reconstruction of the 3D permittivity map from ground-penetrating radar (GPR) data is of great importance for mapping subsurface environments and inspecting underground structural integrity.
A Deep Learning-Based GPR Forward Solver for Predicting B-Scans of Subsurface Objects
To alleviate the computational burden, a deep learning-based 2D GPR forward solver is proposed to predict the GPR B-scans of subsurface objects buried in the heterogeneous soil.
Accurate Tree Roots Positioning and Sizing over Undulated Ground Surfaces by Common Offset GPR Measurements
Normally, the ground surface above the tree roots is assumed to be flat, and standard processing methods based on hyperbolic fitting are applied to the hyperbolae reflection patterns of tree roots for detection purposes.
Dual-Cross-Polarized GPR Measurement Method for Detection and Orientation Estimation of Shallowly Buried Elongated Object
In addition, we present a dual-cross-polarized orientation estimation (DCPOE) algorithm to estimate the object orientation from the two cross-polarized data.
DMRF-UNet: A Two-Stage Deep Learning Scheme for GPR Data Inversion under Heterogeneous Soil Conditions
In the first stage, a U-shape DNN with multi-receptive-field convolutions (MRF-UNet1) is built to remove the clutters due to inhomogeneity of the heterogeneous soil.
SFCW GPR tree roots detection enhancement by time frequency analysis in tropical areas
In high moisture tropical areas such as Singapore, tree fall due to root rot can cause loss of lives and properties.
Slice-Connection Clustering Algorithm for Tree Roots Recognition in Noisy 3D GPR Data
3D mapping of tree roots is a popular ground-penetrating radar (GPR) application.
Estimating Parameters of the Tree Root in Heterogeneous Soil Environments via Mask-Guided Multi-Polarimetric Integration Neural Network
However, the task of estimating root-related parameters is challenging as the root reflection is a complex function of multiple root parameters and root orientations.
Monitoring Atmospheric Pollutants From Ground-based Observations
Remote sensing analysts continuously monitor the amount of pollutants in the atmosphere.
Detecting Blurred Ground-based Sky/Cloud Images
To the best of our knowledge, our approach is the first of its kind in the automatic identification of blurred images for ground-based sky/cloud images.
The Orientation Estimation of Elongated Underground Objects via Multi-Polarization Aggregation and Selection Neural Network
The horizontal orientation angle and vertical inclination angle of an elongated subsurface object are key parameters for object identification and imaging in ground penetrating radar (GPR) applications.
Compact Dual-Polarized Vivaldi Antenna for Ground Penetrating Radar (GPR) Application
In this paper, a compact dual-polarized Vivaldi antenna is presented.
Forecasting Precipitable Water Vapor Using LSTMs
Long-Short-Term-Memory (LSTM) networks have been used extensively for time series forecasting in recent years due to their ability of learning patterns over different periods of time.
Subjective Quality Assessment of Ground-based Camera Images
Image quality assessment is critical to control and maintain the perceived quality of visual content.
Estimating Solar Irradiance Using Sky Imagers
Unlike pyranometers, such sky images contain information about cloud coverage and can be used to derive cloud movement.
CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation
In the existing literature, however, analysis of daytime and nighttime images is considered separately, mainly because of differences in image characteristics and applications.
Multi-label Cloud Segmentation Using a Deep Network
Different empirical models have been developed for cloud detection.
Analyzing Solar Irradiance Variation From GPS and Cameras
The total amount of solar irradiance falling on the earth's surface is an important area of study amongst the photo-voltaic (PV) engineers and remote sensing analysts.
High-Dynamic-Range Imaging for Cloud Segmentation
It is thus difficult to capture the details of an entire scene with a regular camera in a single shot.
Correlating Satellite Cloud Cover with Sky Cameras
The role of clouds is manifold in understanding the various events in the atmosphere, and also in studying the radiative balance of the earth.
Study of Clear Sky Models for Singapore
The estimation of total solar irradiance falling on the earth's surface is important in the field of solar energy generation and forecasting.
Analyzing Cloud Optical Properties Using Sky Cameras
It is important to study the various cloud properties, as it impacts the total solar irradiance falling on the earth's surface.
Nighttime sky/cloud image segmentation
An accurate segmentation of sky/cloud images is already challenging because of the clouds' non-rigid structure and size, and the lower and less stable illumination of the night sky increases the difficulty.
Design of low-cost, compact and weather-proof whole sky imagers for high-dynamic-range captures
Ground-based whole sky imagers are popular for monitoring cloud formations, which is necessary for various applications.
Cloud Radiative Effect Study Using Sky Camera
weather reporting, climate forecasting, and solar energy generation.
Systematic study of color spaces and components for the segmentation of sky/cloud images
Sky/cloud imaging using ground-based Whole Sky Imagers (WSI) is a cost-effective means to understanding cloud cover and weather patterns.
Rough Set Based Color Channel Selection
Color channel selection is essential for accurate segmentation of sky and clouds in images obtained from ground-based sky cameras.
Detecting Rainfall Onset Using Sky Images
Ground-based sky cameras (popularly known as Whole Sky Imagers) are increasingly used now-a-days for continuous monitoring of the atmosphere.
Short-term prediction of localized cloud motion using ground-based sky imagers
Fine-scale short-term cloud motion prediction is needed for several applications, including solar energy generation and satellite communications.
Color-based Segmentation of Sky/Cloud Images From Ground-based Cameras
Sky/cloud images captured by ground-based cameras (a. k. a.
Machine Learning Techniques and Applications For Ground-based Image Analysis
Ground-based whole sky cameras have opened up new opportunities for monitoring the earth's atmosphere.
Estimation of solar irradiance using ground-based whole sky imagers
Ground-based whole sky imagers (WSIs) can provide localized images of the sky of high temporal and spatial resolution, which permits fine-grained cloud observation.
WAHRSIS: A Low-cost, High-resolution Whole Sky Imager With Near-Infrared Capabilities
Cloud imaging using ground-based whole sky imagers is essential for a fine-grained understanding of the effects of cloud formations, which can be useful in many applications.
