A deep-learning model for the amplitude inversion of internal waves based on optical remote-sensing images

Pan, Xiaoju; Wang, Jing; Zhang, Xudong; Mei, Yuan; Shi, Lu; Zhong, Guoqiang

doi:10.1080/01431161.2017.1390269

Cited by 19 publications

(11 citation statements)

References 18 publications

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“…Since V w = V h , we can find that inequality V s < V h holds. According to Equations (7) and (8), in order to save energy of a drone, the following constraints need to be satisfied:…”

Section: Symbol Descriptionmentioning

confidence: 99%

“…On the one hand, a satellite device can cover a large area and is suitable for a wide range of disaster monitoring. On the other hand, satellite technology is susceptible to weather and has a low spatial resolution, making it challenging to meet the need for pest and disease monitoring in agricultural fields [7]. Now, the remote-sensing technology with low-altitude (e.g., drones) has the characteristics of high flexibility and image definition [8], which can meet the requirements of pest and disease monitoring for crops.…”

Section: Introductionmentioning

confidence: 99%

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A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles

Gao

Sun

et al. 2020

Sensors

110

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With the development of information technology, Internet-of-Things (IoT) and low-altitude remote-sensing technology represented by Unmanned Aerial Vehicles (UAVs) are widely used in environmental monitoring fields. In agricultural modernization, IoT and UAV can monitor the incidence of crop diseases and pests from the ground micro and air macro perspectives, respectively. IoT technology can collect real-time weather parameters of the crop growth by means of numerous inexpensive sensor nodes. While depending on spectral camera technology, UAVs can capture the images of farmland, and these images can be utilize for analyzing the occurrence of pests and diseases of crops. In this work, we attempt to design an agriculture framework for providing profound insights into the specific relationship between the occurrence of pests/diseases and weather parameters. Firstly, considering that most farms are usually located in remote areas and far away from infrastructure, making it hard to deploy agricultural IoT devices due to limited energy supplement, a sun tracker device is designed to adjust the angle automatically between the solar panel and the sunlight for improving the energy-harvesting rate. Secondly, for resolving the problem of short flight time of UAV, a flight mode is introduced to ensure the maximum utilization of wind force and prolong the fight time. Thirdly, the images captured by UAV are transmitted to the cloud data center for analyzing the degree of damage of pests and diseases based on spectrum analysis technology. Finally, the agriculture framework is deployed in the Yangtze River Zone of China and the results demonstrate that wheat is susceptible to disease when the temperature is between 14 • C and 16 • C, and high rainfall decreases the spread of wheat powdery mildew.

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“…Since V w = V h , we can find that inequality V s < V h holds. According to Equations (7) and (8), in order to save energy of a drone, the following constraints need to be satisfied:…”

Section: Symbol Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles

Gao

Sun

et al. 2020

Sensors

110

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“…This paper concludes that usage of texture characteristic parameters of optical remote sensing images are best to determine the amplitude of internal waves. AUV (Autonomous underwater vehicle) are also used in [11] for detection of internal waves. Using of multiple AUV's will give best results.…”

Section: Literature Surveymentioning

confidence: 99%

Ocean Internal Wave Detection from SAR Images using Particle Swarm Optimization

Divya

Vasavi

Sarma

2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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The internal waves are the waves that occur in the deep ocean. Many researchers found different methods for the detection of internal waves. As we all know that machine learning is the fastest growing technology to solve complicated problems. So, here in this paper we took this advantage for the detection of internal waves because this is also considered as a complicated problem. Based on this a novel method is proposed for internal wave detection using neural networks which is a method that acts as a human brain. A novel system using Particle swarm optimization is proposed in this paper to automatically detect internal waves. Such system can differentiate with surface wavelets and slicks. This paper describes extraction of internal wave parameters from SAR images. Initially ocean internal wave detection is modeled based on optimization algorithms. In the proposed method, two models such as feature representation model and prediction model will be build.

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“…The relation of total perceptible water and observation data in different channels was built using machine learning techniques. Pan et al [32] applied one of the machine learning approaches, the back-propagation (BP) algorithm, to retrieve the amplitude of IWs using texture information of satellite images and ocean environmental parameters. Li et al [33] applied deep learning techniques to extract IW signals from satellite images and systematically reviewed the application of machine learning techniques to the information mining of remote sensing imagery.…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning Model for Forecasting Internal Wave Propagation in the Andaman Sea

Zhang

Zheng

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Internal waves (IWs) are broadly distributed globally and have significant impacts on offshore engineering and underwater navigation. The prediction of IW propagation is a challenging task because of the complex factors involved. In this study, a machine-learning model was developed to predict IW propagation in the Andaman Sea. The model is based on a back-propagation neural network trained by 1189 IW samples, including the crest length and the peak-to-peak distance of IWs, extracted from 123 Moderate-Resolution Imaging Spectroradiometer (MODIS) images and 33 Ocean Land Color Instrument (OLCI) images acquired from 2015 to 2019 and corresponding ocean environment parameters. Using the leading wave crest within an IW packet as input, we ran the model to forecast the IW locations and compare them with satellite observations. The average root-mean-square difference between the model-forecasted and satellite-observed IW leading crest after one tidal cycle was 3.21 km. The corresponding averaged correlation coefficient was 0.95 and the average Fréchet Distance was 11.46 km. We reiterated the model run over two tidal periods and obtained similar statistical results, indicating the robustness of forecasting IW packets. We find that reducing the time step helped to improve forecasting accuracy. The influence of input errors and seasonal variations on model results are discussed and an analysis shows that the initial propagation direction introduced to the model is necessary for cross-propagating IW patterns. Comparisons with the Korteweg-de Vries equation results show that the developed model has better performance and is more robust.

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A deep-learning model for the amplitude inversion of internal waves based on optical remote-sensing images

Cited by 19 publications

References 18 publications

A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles

A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles

Ocean Internal Wave Detection from SAR Images using Particle Swarm Optimization

A Machine-Learning Model for Forecasting Internal Wave Propagation in the Andaman Sea

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