A microcomputer system for remotely sensed image data on ecological environments

1986 ◽  
Vol 32 (1-3) ◽  
pp. 15-27
Author(s):  
Yuzo Suga ◽  
Tanehiro Futagami ◽  
Kaneo Okano ◽  
Sotaro Tanaka ◽  
Toshiro Sugimura
Keyword(s):  
Image Data ◽  
Remotely Sensed ◽  
Microcomputer System ◽  
Remotely Sensed Image

A Successive Decision Tree Approach to Mining Remotely Sensed Image Data

2008 ◽  
pp. 2978-2992
Author(s):  
Jianting Zhang ◽  
Wieguo Liu ◽  
Le Gruenwald
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Classification Accuracy ◽  
Decision Rules ◽  
Image Data ◽  
Remotely Sensed ◽  
Fast Speed ◽  
Remotely Sensed Image ◽  
Tree Approach

Decision trees (DT) has been widely used for training and classification of remotely sensed image data due to its capability to generate human interpretable decision rules and its relatively fast speed in training and classification. This chapter proposes a successive decision tree (SDT) approach where the samples in the ill-classified branches of a previous resulting decision tree are used to construct a successive decision tree. The decision trees are chained together through pointers and used for classification. SDT aims at constructing more interpretable decision trees while attempting to improve classification accuracies. The proposed approach is applied to two real remotely sensed image datasets for evaluations in terms of classification accuracy and interpretability of the resulting decision rules.

A Successive Decision Tree Approach to Mining Remotely Sensed Image Data

2011 ◽  
pp. 98-112
Author(s):  
Jianting Zhang ◽  
Wieguo Liu ◽  
Le Gruenwald
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Classification Accuracy ◽  
Decision Rules ◽  
Image Data ◽  
Remotely Sensed ◽  
Fast Speed ◽  
Remotely Sensed Image ◽  
Tree Approach

Decision trees (DT) has been widely used for training and classification of remotely sensed image data due to its capability to generate human interpretable decision rules and its relatively fast speed in training and classification. This chapter proposes a successive decision tree (SDT) approach where the samples in the ill-classified branches of a previous resulting decision tree are used to construct a successive decision tree. The decision trees are chained together through pointers and used for classification. SDT aims at constructing more interpretable decision trees while attempting to improve classification accuracies. The proposed approach is applied to two real remotely sensed image datasets for evaluations in terms of classification accuracy and interpretability of the resulting decision rules.

Atmospheric correction of remotely sensed image data by a simplified model

1992 ◽  
Vol 97 (D17) ◽  
pp. 18797 ◽  
Author(s):  
R. C. Wrigley ◽  
M. A. Spanner ◽  
R. E. Slye ◽  
R. F. Pueschel ◽  
H. R. Aggarwal
Keyword(s):  
Atmospheric Correction ◽  
Image Data ◽  
Remotely Sensed ◽  
Simplified Model ◽  
Remotely Sensed Image
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