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Remote Sensing Science

Remote Sensing Science is an international comprehensive professional academic journal of Ivy Publisher, concerning the development of remote sensing science and technology. The main focus of the journal is the academic papers and comments of latest improvement in the fields of basic theory, technology development and application of remote sensing science, report of latest research result, aiming at providing a good communication platform to tran... [More] Remote Sensing Science is an international comprehensive professional academic journal of Ivy Publisher, concerning the development of remote sensing science and technology. The main focus of the journal is the academic papers and comments of latest improvement in the fields of basic theory, technology development and application of remote sensing science, report of latest research result, aiming at providing a good communication platform to transfer, share and discuss the theoretical and technical development of remote-sensing theory development for professionals, scholars, researchers and administrative staffs in this field, reflecting the academic front level, promote academic change and seize the theory, practice front line, research level and development direction of remote sensing science.

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ISSN Print:2329-8138

ISSN Online:2329-8146

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Paper Infomation

Retrieval of Optical Remote Sensing Image Content Based on Convolution Neural Network

Full Text(PDF, 822KB)

Author: Tengfei Ji, Yameng Zhao, Tao Yu, Bing Zhou, Xiangzhi Huang

Abstract: The insufficiency of image detection method based on bottom feature extraction is analyzed and an optical remote sensing image content retrieval method based on convolution neural network is proposed. Firstly, convolutional neural network is used to train the rsscn7-master remote sensing image data set of the training sample. Image features are extracted from the sample to build the image feature library. Then, Sofemax classifier is used to classify the feature map to achieve precise classification and improve the accuracy of the model. Then, the model is tested with the help of the sample map or the actual optical remote sensing image map, and good experimental results are obtained. Experimental results show that this method is effective in the content retrieval of optical remote sensing images and has high retrieval accuracy.

Keywords: Remote Sensing Image Content Retrieval, Image Classification, Convolution Neural Network, Dropout, Sofemax Classifier, RSSCN7 - master Data Set

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