Prediction of forest stem volume using kriging adapted to detected edges

2002 ◽  
Vol 32 (3) ◽  
pp. 509-518 ◽  
Author(s):  
Jörgen Wallerman ◽  
Steve Joyce ◽  
Coomaren P Vencatasawmy ◽  
Håkan Olsson
Keyword(s):  
Interpolation Method ◽  
Image Data ◽  
Field Measurements ◽  
Spatial Prediction ◽  
Detection Algorithm ◽  
New Method ◽  
Planning System ◽  
Stem Volume ◽  
Landsat Tm Image ◽  
Very High Spatial Resolution

The modern techniques of the global positioning system and geographic information system enable many new approaches to forestry planning problems. Using these it is possible to efficiently geoposition, store, and analyze each field measurement in a spatial context. This work is directed towards the application of a dynamic forestry planning system based on a forest map with very high spatial resolution created from geopositioned field plot data, instead of the traditional forest stand map. The new dynamic system is dependent on accurate methods to create a high-resolution map from a set of field measurements. This problem may be solved using the kriging spatial prediction (interpolation) method. The aim of this paper is to present and empirically evaluate a new kriging method side-by-side with global and stratified kriging. The new method uses the output from an edge-detection algorithm, here applied to Landsat TM image data, to increase the prediction accuracy. Prediction evaluation was made in terms of mean forest stem volume per hectare measured on circular field plots of 10 m radius. The new method showed a prediction root mean square error of 41% of the mean volume, compared with corresponding results of global, 58%, and stratified kriging, 45%.

scholarly journals Generation and Annotation of Simulation-Real Ship Images for Convolutional Neural Networks Training and Testing

2021 ◽  
Vol 11 (13) ◽  
pp. 5931
Author(s):  
Ji’an You ◽  
Zhaozheng Hu ◽  
Chao Peng ◽  
Zhiqiang Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Annotation ◽  
Three Dimensional ◽  
Image Data ◽  
Detection Algorithm ◽  
Simulation Software ◽  
High Quality ◽  
Annotation Method ◽  
Detection Of Objects

Large amounts of high-quality image data are the basis and premise of the high accuracy detection of objects in the field of convolutional neural networks (CNN). It is challenging to collect various high-quality ship image data based on the marine environment. A novel method based on CNN is proposed to generate a large number of high-quality ship images to address this. We obtained ship images with different perspectives and different sizes by adjusting the ships’ postures and sizes in three-dimensional (3D) simulation software, then 3D ship data were transformed into 2D ship image according to the principle of pinhole imaging. We selected specific experimental scenes as background images, and the target ships of the 2D ship images were superimposed onto the background images to generate “Simulation–Real” ship images (named SRS images hereafter). Additionally, an image annotation method based on SRS images was designed. Finally, the target detection algorithm based on CNN was used to train and test the generated SRS images. The proposed method is suitable for generating a large number of high-quality ship image samples and annotation data of corresponding ship images quickly to significantly improve the accuracy of ship detection. The annotation method proposed is superior to the annotation methods that label images with the image annotation software of Label-me and Label-img in terms of labeling the SRS images.

A New Method of Field Measurements for Small-Aperture Magnets

1991 ◽  
Vol 30 (Part 1, No. 9A) ◽  
pp. 2082-2084
Author(s):  
Mitsuo Kikuchi ◽  
Hisayoshi Nakayama
Keyword(s):  
Field Measurements ◽  
New Method ◽  
Small Aperture

IMPROVED TOW-END DETECTION FOR FIBER PLACEMENT INSPECTION USING MACHINE LEARNING

2021 ◽  
Author(s):  
ADRIANA W. (AGNES) BLOM-SCHIEBER ◽  
WEI GUO ◽  
EKTA SAMANI ◽  
ASHIS BANERJEE
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Image Data ◽  
Detection Algorithm ◽  
Automated Inspection ◽  
Machine Learning Algorithm ◽  
Fiber Placement ◽  
Inspection Systems ◽  
Automated Fiber Placement ◽  
The University

A machine learning approach to improve the detection of tow ends for automated inspection of fiber-placed composites is presented. Automated inspection systems for automated fiber placement processes have been introduced to reduce the time it takes to inspect plies after they are laid down. The existing system uses image data from ply boundaries and a contrast-based algorithm to locate the tow ends in these images. This system fails to recognize approximately 10% of the tow ends, which are then presented to the operator for manual review, taking up precious time in the production process. An improved tow end detection algorithm based on machine learning is developed through a research project with the Boeing Advanced Research Center (BARC) at the University of Washington. This presentation shows the preprocessing, neural network and post‐processing steps implemented in the algorithm, and the results achieved with the machine learning algorithm. The machine learning algorithm resulted in a 90% reduction in the number of undetected tows compared to the existing system.

scholarly journals Automated single particle detection and tracking for large microscopy datasets

2016 ◽  
Vol 3 (5) ◽  
pp. 160225 ◽  
Author(s):  
Rhodri S. Wilson ◽  
Lei Yang ◽  
Alison Dun ◽  
Annya M. Smyth ◽  
Rory R. Duncan ◽  
...  
Keyword(s):  
Single Molecule ◽  
Single Particle ◽  
Image Data ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Large Datasets ◽  
Single Particle Tracking ◽  
Synthetic Image ◽  
Particle Detection ◽  
Very Large Datasets

Recent advances in optical microscopy have enabled the acquisition of very large datasets from living cells with unprecedented spatial and temporal resolutions. Our ability to process these datasets now plays an essential role in order to understand many biological processes. In this paper, we present an automated particle detection algorithm capable of operating in low signal-to-noise fluorescence microscopy environments and handling large datasets. When combined with our particle linking framework, it can provide hitherto intractable quantitative measurements describing the dynamics of large cohorts of cellular components from organelles to single molecules. We begin with validating the performance of our method on synthetic image data, and then extend the validation to include experiment images with ground truth. Finally, we apply the algorithm to two single-particle-tracking photo-activated localization microscopy biological datasets, acquired from living primary cells with very high temporal rates. Our analysis of the dynamics of very large cohorts of 10 000 s of membrane-associated protein molecules show that they behave as if caged in nanodomains. We show that the robustness and efficiency of our method provides a tool for the examination of single-molecule behaviour with unprecedented spatial detail and high acquisition rates.

scholarly journals Pothole Classification Model Using Edge Detection in Road Image

2020 ◽  
Vol 10 (19) ◽  
pp. 6662
Author(s):  
Ji-Won Baek ◽  
Kyungyong Chung
Keyword(s):  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Image Data ◽  
Structural Similarity ◽  
Detection Algorithm ◽  
Classification Model ◽  
Specific Object ◽  
Pixel Value ◽  
Distortion Rate

Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In addition, to extract the characteristics of a pothole, the contour of the pothole is extracted through edge detection. Finally, potholes are detected and classified based by the (you only look once) YOLO algorithm. The performance evaluation evaluates the distortion rate and restoration rate of the image, and the validity of the model and accuracy of the classification. The result of the evaluation shows that the mean square error (MSE) of the distortion rate and restoration rate of the proposed method has errors of 0.2–0.44. The peak signal to noise ratio (PSNR) is evaluated as 50 db or higher. The structural similarity index map (SSIM) is evaluated as 0.71–0.82. In addition, the result of the pothole classification shows that the area under curve (AUC) is evaluated as 0.9.

scholarly journals A New Restoration Method for Radio Frequency Interference Effects on AMSR-2 over North America

2019 ◽  
Vol 11 (24) ◽  
pp. 2917 ◽  
Author(s):  
Wangbin Shen ◽  
Zhengkun Qin ◽  
Zhaohui Lin
Keyword(s):  
Radio Frequency ◽  
Land Surface ◽  
Interpolation Method ◽  
Low Frequency ◽  
Principal Component ◽  
New Method ◽  
Radio Frequency Interference ◽  
Data Restoration ◽  
Order Of Magnitude ◽  
Restoration Method

Observations from spaceborne microwave imagers are important sources of land surface information. However, the low-frequency channels of microwave imagers are easily interfered with by active radio signals with similar frequencies. Radio frequency interference (RFI) signals are widely distributed because of the lack of frequency protection, which seriously hinders the application of microwave imager data in data assimilation and retrieval research. In this paper, a new data restoration method is proposed based on principal component analysis (PCA). Both the ideal and real reconstruction experiments show that the new method can effectively repair abnormal observations interfered by RFI compared with the commonly used Cressman interpolation method because observation information over the whole selected domain is used for restoration in the new method, whereas Cressman interpolation uses only a selection of data around the target observation. The observation errors in the data with RFI can be reduced by one order of magnitude by means of the new method and little artificial information is introduced. One-week restoration validation also proves that the new method has a stable accuracy and broad application prospects.

Sign in / Sign up

Export Citation Format

Share Document