Content-based high-resolution remote sensing image retrieval with local binary patterns

2006 ◽  
Author(s):  
A. P. Wang ◽  
S. G. Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Retrieval ◽  
Local Binary Patterns ◽  
Remote Sensing Image

High Resolution Remote Sensing Image Retrieval Using Quin-Tree and Multi-Feature Histogram

2010 ◽  
Vol 12 (2) ◽  
pp. 275-280 ◽  
Author(s):  
Qiming WAN ◽  
Min WANG ◽  
Xingyue ZHANG ◽  
Shen JIANG ◽  
Yulin XIE
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Retrieval ◽  
Remote Sensing Image

scholarly journals Low Dimensional Discriminative Representation of Fully Connected Layer Features Using Extended LargeVis Method for High-Resolution Remote Sensing Image Retrieval

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4718
Author(s):  
Zheng Zhuo ◽  
Zhong Zhou
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Dimensionality Reduction ◽  
Image Retrieval ◽  
High Dimensional Data ◽  
Remote Sensing Image ◽  
High Dimensional ◽  
Support Vector ◽  
Low Dimensional ◽  
Fully Connected

Recently, there have been rapid advances in high-resolution remote sensing image retrieval, which plays an important role in remote sensing data management and utilization. For content-based remote sensing image retrieval, low-dimensional, representative and discriminative features are essential to ensure good retrieval accuracy and speed. Dimensionality reduction is one of the important solutions to improve the quality of features in image retrieval, in which LargeVis is an effective algorithm specifically designed for Big Data visualization. Here, an extended LargeVis (E-LargeVis) dimensionality reduction method for high-resolution remote sensing image retrieval is proposed. This can realize the dimensionality reduction of single high-dimensional data by modeling the implicit mapping relationship between LargeVis high-dimensional data and low-dimensional data with support vector regression. An effective high-resolution remote sensing image retrieval method is proposed to obtain stronger representative and discriminative deep features. First, the fully connected layer features are extracted using a channel attention-based ResNet50 as a backbone network. Then, E-LargeVis is used to reduce the dimensionality of the fully connected features to obtain a low-dimensional discriminative representation. Finally, L2 distance is computed for similarity measurement to realize the retrieval of high-resolution remote sensing images. The experimental results on four high-resolution remote sensing image datasets, including UCM, RS19, RSSCN7, and AID, show that for various convolutional neural network architectures, the proposed E-LargeVis can effectively improve retrieval performance, far exceeding other dimensionality reduction methods.

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