scholarly journals Land Cover Classification of RapidEye Satellite Images Using Tesseled Cap Transformation (TCT)

2017 ◽  
Vol 33 (1) ◽  
pp. 79-88 ◽  
Author(s):  
Hogyung Moon ◽  
Taeyoung Choi ◽  
Guhyeok Kim ◽  
Nyunghee Park ◽  
Honglyun Park ◽  
...  
Keyword(s):  
Land Cover ◽  
Satellite Images ◽  
Land Cover Classification

scholarly journals LAND COVER CLASSIFICATION OF SATELLITE IMAGES USING CONTEXTUAL INFORMATION

2013 ◽  
Vol II-3/W1 ◽  
pp. 1-6 ◽  
Author(s):  
B. Fröhlich ◽  
E. Bach ◽  
I. Walde ◽  
S. Hese ◽  
C. Schmullius ◽  
...  
Keyword(s):  
Land Cover ◽  
Satellite Images ◽  
Contextual Information ◽  
Land Cover Classification

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

Training Deep Convolutional Neural Networks for Land–Cover Classification of High-Resolution Imagery

2017 ◽  
Vol 14 (4) ◽  
pp. 549-553 ◽  
Author(s):  
Grant J. Scott ◽  
Matthew R. England ◽  
William A. Starms ◽  
Richard A. Marcum ◽  
Curt H. Davis
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Land Cover Classification ◽  
Deep Convolutional Neural Networks ◽  
High Resolution Imagery
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