搜索结果: 1-15 共查到“知识库 理学 Neural”相关记录158条 . 查询时间(0.14 秒)
COMPARATIVE STUDY ON DEEP NEURAL NETWORK MODELS FOR CROP CLASSIFICATION USING TIME SERIES POLSAR AND OPTICAL DATA
Deep neural networks CNNs LSTMs ConvLSTMs Crop classification
2019/2/28
Crop classification is an important task in many crop monitoring applications. Satellite remote sensing has provided easy, reliable, and fast approaches to crop classification task. In this study, a c...
SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL REMOTE SENSING IMAGES USING VARIATIONAL AUTOENCODER AND CONVOLUTION NEURAL NETWORK
Hyperspectral classification feature extraction spectral channels deep learning
2019/2/28
In this paper, we propose a spectral-spatial feature extraction framework based on deep learning (DL) for hyperspectral image (HSI) classification. In this framework, the variational autoencoder (VAE)...
MULTISPECTRAL PANSHARPENING APPROACH USING PULSE-COUPLED NEURAL NETWORK SEGMENTATION
Pansharpening PCNN Image Fusion Multispectral Imaging Remote Sensing Segmentation
2018/5/14
The paper proposes a novel pansharpening method based on the pulse-coupled neural network segmentation. In the new method, uniform injection gains of each region are estimated through PCNN segmentatio...
A NOVEL DEEP CONVOLUTIONAL NEURAL NETWORK FOR SPECTRAL–SPATIAL CLASSIFICATION OF HYPERSPECTRAL DATA
Hyperspectral Data Classification Three-dimensional Convolution Deep CNN Feature Extraction
2018/5/14
Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectr...
THE EXTRACTION OF POST-EARTHQUAKE BUILDING DAMAGE INFORMATIOM BASED ON CONVOLUTIONAL NEURAL NETWORK
Earthquake Seismic Damage Information Extraction Deep Learning Convolutional Neural Network
2018/5/11
The seismic damage information of buildings extracted from remote sensing (RS) imagery is meaningful for supporting relief and effective reduction of losses caused by earthquake. Both traditional pixe...
RESEARCH ON THE DIRECT CARBON EMISSION FORECAST OF CHINA'S PROVINCIAL RESIDENTS BASED ON NEURAL NETWORK
Global climate change Residents’ carbon emissions Elman Neural network Forecast China
2018/5/16
Global climate change, which mainly effected by human carbon emissions, would affect the regional economic, natural ecological environment, social development and food security in the near future. It’...
SEMANTIC SEGMENTATION OF CONVOLUTIONAL NEURAL NETWORK FOR SUPERVISED CLASSIFICATION OF MULTISPECTRAL REMOTE SENSING
Semantic Segmentation Multi Spectral Remote Sensing Convolutional Neural Network U-net multi-scale image
2018/5/16
Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the classification of roads, vegetation, buildings and water from remot...
EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD
EIGENENTROPY CONVOLUTIONAL NEURAL NETWORK ALS POINT CLOUDS CLASSIFICATION METHOD
2018/5/16
The classification of point clouds is the first step in the extraction of various types of geo-information form point clouds. Recently the ISPRS WG II/4 provides a benchmark on 3D semantic labelling, ...
SPATIAL DATA MINING TOOLBOX FOR MAPPING SUITABILITY OF LANDFILL SITES USING NEURAL NETWORKS
Spatial data mining GIS neural networks ArcGIS toolbox landfill suitability analysis
2016/10/14
Mapping the suitability of landfill sites is a complex field and is involved with multidiscipline. The purpose of this research is to create an ArcGIS spatial data mining toolbox for mapping the suita...
Long-term trends in f0 F2 over Grahamstown using Neural Networks
Solar activity magnetic activity
2015/9/25
Many authors have claimed to have found long-term trends in f0 F2 , or the lack thereof, for different stations. Such investigations usually involve gross assumptions about the variation of f0 F2 with...
A new approach for residual gravity anomaly profile interpretations: Forced Neural Network (FNN)
Forced Neural Network gravity anomaly modeling synthetic model Gulf of Mexico
2015/9/8
This paper presents a new approach for interpretation of residual gravity anomaly profiles, assuming horizontal
cylinders as source. The new method, called Forced Neural Network (FNN), is introduced ...
Tectonic modeling of Konya-Beysehir Region (Turkey) using cellular neural networks
Cellular Neural Network (CNN) forward and inverse modeling tectonic modeling boundary analysis Central Turkey
2015/9/2
In this paper, to separate regional-residual anomaly maps and to detect borders of buried geological bodies, we
applied the Cellular Neural Network (CNN) approach to gravity and magnetic anomaly maps...
Using neural networks to study the geomagnetic field evolution
Geomagnetic Field Geomagnetic Observatory Neural Networks (NN) time series time prediction
2015/9/1
study their time evolution in years. In order to find the best NN for the time predictions, we tested many different
kinds of NN and different ways of their training, when the inputs and targets are ...
Parameter estimation for bursting neural models
bursting neural models Parameter estimation
2015/8/25
This paper presents work on parameter estimation
methods for bursting neural models. In our
approach we use both geometrical features specific to
bursting, as well as general features such as perio...