Detection Probability for Moving Ground Target of Normal Distribution Using an Imaging Satellite

Guo, Zhaoming; Jiang, Yi; Bi, Shihua

doi:10.1049/cje.2018.08.005

Cited by 4 publications

(2 citation statements)

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“…Network. Convolution neural network (CNN) is a typical network structure in deep learning model [14]. Different from traditional machine learning, convolution neural network can be better used for image and time series data processing, especially for image classification and language recognition.…”

Section: Basic Eory Of Convolution Neuralmentioning

confidence: 99%

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Deep Learning Model of Image Classification Using Machine Learning

Lv¹,

Zhang²,

Wang³

2022

Advances in Multimedia

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Not only were traditional artificial neural networks and machine learning difficult to meet the processing needs of massive images in feature extraction and model training but also they had low efficiency and low classification accuracy when they were applied to image classification. Therefore, this paper proposed a deep learning model of image classification, which aimed to provide foundation and support for image classification and recognition of large datasets. Firstly, based on the analysis of the basic theory of neural network, this paper expounded the different types of convolution neural network and the basic process of its application in image classification. Secondly, based on the existing convolution neural network model, the noise reduction and parameter adjustment were carried out in the feature extraction process, and an image classification depth learning model was proposed based on the improved convolution neural network structure. Finally, the structure of the deep learning model was optimized to improve the classification efficiency and accuracy of the model. In order to verify the effectiveness of the deep learning model proposed in this paper in image classification, the relationship between the accuracy of several common network models in image classification and the number of iterations was compared through experiments. The results showed that the model proposed in this paper was better than other models in classification accuracy. At the same time, the classification accuracy of the deep learning model before and after optimization was compared and analyzed by using the training set and test set. The results showed that the accuracy of image classification had been greatly improved after the model proposed in this paper had been optimized to a certain extent.

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Section: Basic Eory Of Convolution Neuralmentioning

confidence: 99%

“…en, the improved convolution neural network model is used to extract the image feature information, and the Softmax classifier is used to classify the features [14].…”

Section: Convolution Neural Network Modelmentioning

confidence: 99%