Next Article in Journal
A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5
Next Article in Special Issue
Identify Informative Bands for Hyperspectral Target Detection Using the Third-Order Statistic
Previous Article in Journal
Improving DGNSS Performance through the Use of Network RTK Corrections
Previous Article in Special Issue
Integrating MNF and HHT Transformations into Artificial Neural Networks for Hyperspectral Image Classification
Article

Improving Land Cover Classification Using Genetic Programming for Feature Construction

1
LASIGE, Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal
2
Forest Research Centre, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal
3
NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Edoardo Pasolli
Remote Sens. 2021, 13(9), 1623; https://doi.org/10.3390/rs13091623
Received: 11 March 2021 / Revised: 12 April 2021 / Accepted: 16 April 2021 / Published: 21 April 2021
(This article belongs to the Special Issue Advanced Machine Learning Approaches for Hyperspectral Data Analysis)
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. View Full-Text
Keywords: genetic programming; evolutionary computation; machine learning; classification; multiclass classification; feature construction; hyperfeatures; spectral indices genetic programming; evolutionary computation; machine learning; classification; multiclass classification; feature construction; hyperfeatures; spectral indices
Show Figures

Graphical abstract

MDPI and ACS Style

Batista, J.E.; Cabral, A.I.R.; Vasconcelos, M.J.P.; Vanneschi, L.; Silva, S. Improving Land Cover Classification Using Genetic Programming for Feature Construction. Remote Sens. 2021, 13, 1623. https://doi.org/10.3390/rs13091623

AMA Style

Batista JE, Cabral AIR, Vasconcelos MJP, Vanneschi L, Silva S. Improving Land Cover Classification Using Genetic Programming for Feature Construction. Remote Sensing. 2021; 13(9):1623. https://doi.org/10.3390/rs13091623

Chicago/Turabian Style

Batista, João E., Ana I.R. Cabral, Maria J.P. Vasconcelos, Leonardo Vanneschi, and Sara Silva. 2021. "Improving Land Cover Classification Using Genetic Programming for Feature Construction" Remote Sensing 13, no. 9: 1623. https://doi.org/10.3390/rs13091623

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop