Low-Complexity Multispectral Images Compression Algorithm Based Distributed Compressive Sensing

2013 ◽  
Author(s):  
Longxu Jin ◽  
Jin Li ◽  
Min Zhang ◽  
Yinan Wu
Keyword(s):  
Compressive Sensing ◽  
Low Complexity ◽  
Compression Algorithm ◽  
Multispectral Images ◽  
Distributed Compressive Sensing

Distributed Compressive Sensing

2009 ◽  
Author(s):  
Dror Baron ◽  
Marco F. Duarte ◽  
Michael B. Wakin ◽  
Shriram Sarvotham ◽  
Richard G. Baraniuk
Keyword(s):  
Compressive Sensing ◽  
Distributed Compressive Sensing

scholarly journals Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1994
Author(s):  
Qian Ma ◽  
Wenting Han ◽  
Shenjin Huang ◽  
Shide Dong ◽  
Guang Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Confusion Matrix ◽  
Object Oriented ◽  
Low Complexity ◽  
Classification Model ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Multispectral Images ◽  
High Complexity ◽  
Structure Complexity

This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models’ classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models’ overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures.

scholarly journals Low-complexity compressive sensing with downsampling

2014 ◽  
Vol 11 (3) ◽  
pp. 20130947-20130947 ◽  
Author(s):  
Dongeun Lee ◽  
Jaesik Choi ◽  
Heonshik Shin
Keyword(s):  
Compressive Sensing ◽  
Low Complexity

Distributed compressive sensing of light field

2015 ◽  
Author(s):  
Rui Lei ◽  
Wei Shen ◽  
Zhi-jiang Zhang ◽  
Ying Zhou
Keyword(s):  
Compressive Sensing ◽  
Light Field ◽  
Distributed Compressive Sensing
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