Robustness evaluation of remote-sensing image feature detectors with TH priori-information data set

Duan, Yiping; Tao, Xiaoming; Liu, Xijia; Ge, Ning

doi:10.23919/jcc.2020.10.016

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…We need a dividing line/surface that can separate the two classes. The distance from the closest point to it in the two classes is equal to the dividing line/surface, and we want this interval to be as long as possible [24].…”

Section: ) Support Vector Machinementioning

confidence: 99%

RETRACTED ARTICLE: Big data classification of remote sensing image based on cloud computing and convolutional neural network

2022

Soft Comput

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In recent years, with the continuous development of cloud computing and big data technology, information technology is penetrating all corners of enterprise development at a speed that ordinary people cannot imagine. Today's remote sensing image classification plays an important role in many applications. This article is based on cloud computing and convolutional neural network to study the remote sensing image big data classification. This article takes cloud computing technology and convolutional neural network technology as the technical points. First, it introduces cloud computing technology in more detail from the basic concepts, characteristics and classification of cloud computing, and then compares traditional methods from the selection of feature extraction methods and classifiers.The remote sensing image classification method is described, and finally the convolutional neural network model that needs to be applied in this research is explained. In the experiment, the features extracted by multiple pre-training networks are fused. Through the study of the three feature fusion methods, it is found that the classification accuracy rate has been further improved on the three data sets. The experimental results in this paper show that the remote sensing image big data classification method based on cloud computing and convolutional neural network has more than a little improvement in accuracy than the traditional remote sensing image classification method, and the accuracy rate has reached 86.3%.

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Section: ) Support Vector Machinementioning

confidence: 99%

RETRACTED ARTICLE: Big data classification of remote sensing image based on cloud computing and convolutional neural network

2022

Soft Comput

View full text Add to dashboard Cite

show abstract

Big Data Classification of Remote Sensing Image Based on Cloud Computing and Convolutional Neural Network

2021

Preprint

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In recent years, with the continuous development of cloud computing and big data technology, information technology is penetrating all corners of enterprise development at a speed that ordinary people cannot imagine. Today's remote sensing image classification plays an important role in many applications. This article is based on cloud computing and convolutional neural network to study the remote sensing image big data classification. This article takes cloud computing technology and convolutional neural network technology as the technical points. First, it introduces cloud computing technology in more detail from the basic concepts, characteristics and classification of cloud computing, and then compares traditional methods from the selection of feature extraction methods and classifiers. The remote sensing image classification method is described, and finally the convolutional neural network model that needs to be applied in this research is explained. In the experiment, the features extracted by multiple pre-training networks are fused. Through the study of the three feature fusion methods, it is found that the classification accuracy rate has been further improved on the three data sets. The experimental results in this paper show that the remote sensing image big data classification method based on cloud computing and convolutional neural network has more than a little improvement in accuracy than the traditional remote sensing image classification method, and the accuracy rate has reached 86.3%.

show abstract

Robustness evaluation of remote-sensing image feature detectors with TH priori-information data set

Cited by 2 publications

References 6 publications

RETRACTED ARTICLE: Big data classification of remote sensing image based on cloud computing and convolutional neural network

RETRACTED ARTICLE: Big data classification of remote sensing image based on cloud computing and convolutional neural network

Big Data Classification of Remote Sensing Image Based on Cloud Computing and Convolutional Neural Network

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