Low-complexity compression of multispectral images based on classified transform coding

Marco Cagnazzo; Luca Cicala; Giovanni Poggi; Luisa Verdoliva

doi:10.1016/j.image.2006.08.003

Low-complexity compression of multispectral images based on classified transform coding

Signal Processing Image Communication ◽

10.1016/j.image.2006.08.003 ◽

2006 ◽

Vol 21 (10) ◽

pp. 850-861 ◽

Cited By ~ 10

Author(s):

Marco Cagnazzo ◽

Luca Cicala ◽

Giovanni Poggi ◽

Luisa Verdoliva

Keyword(s):

Low Complexity ◽

Transform Coding ◽

Multispectral Images

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Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Sensors ◽

10.3390/s21061994 ◽

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.

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