Optical remote sensing image retrieval based on convolutional neural networks

2018 ◽  
Vol 26 (1) ◽  
pp. 200-207
Author(s):  
李宇 LI Yu ◽  
刘雪莹 LIU Xue-ying ◽  
张洪群 ZHANG Hong-qun ◽  
李湘眷 LI Xiang-juan ◽  
孙晓瑶 SUN Xiao-yao
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Image Retrieval ◽  
Convolutional Neural Networks ◽  
Remote Sensing Image ◽  
Optical Remote Sensing ◽  
Optical Remote Sensing Image

Exploiting representations from pre-trained convolutional neural networks for high-resolution remote sensing image retrieval

2017 ◽  
Vol 77 (13) ◽  
pp. 17489-17515 ◽  
Author(s):  
Yun Ge ◽  
Shunliang Jiang ◽  
Qingyong Xu ◽  
Changlong Jiang ◽  
Famao Ye
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
High Resolution ◽  
Image Retrieval ◽  
Convolutional Neural Networks ◽  
Remote Sensing Image

scholarly journals Mathematical models for information classification and recognition of multi-target optical remote sensing images

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 951-960
Author(s):  
Haiqing Zhang ◽  
Jun Han
Keyword(s):  
Remote Sensing ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Remote Sensing Image ◽  
Optical Remote Sensing ◽  
Specific Class ◽  
Three Dimensional Model ◽  
Image Information ◽  
Information Classification ◽  
Optical Remote Sensing Image

Abstract Traditionally, three-dimensional model is used to classify and recognize multi-target optical remote sensing image information, which can only identify a specific class of targets, and has certain limitations. A mathematical model of multi-target optical remote sensing image information classification and recognition is designed, and a local adaptive threshold segmentation algorithm is used to segment multi-target optical remote sensing image to reduce the gray level between images and improve the accuracy of feature extraction. Remote sensing image information is multi-feature, and multi-target optical remote sensing image information is identified by chaotic time series analysis method. The experimental results show that the proposed model can effectively classify and recognize multi-target optical remote sensing image information. The average recognition rate is more than 95%, the maximum robustness is 0.45, the recognition speed is 98%, and the maximum time-consuming average is only 14.30 s. It has high recognition rate, robustness, and recognition efficiency.

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