Abstract
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.
Highlights
Since the establishment of the Warsaw Framework in 2013, remote sensing (RS) is recommended as an appropriate technology for monitoring and measuring, reporting, and verification (MRV) for countries reporting forest land cover and land cover change to the UNFCCC (https://unfccc.int/topics/land-use/resources/warsaw-framework-for-reddplus)
They are adapted to binary classification by defining a threshold separating the two classes, but there is no easy adaptation for multiclass problems, the reason why we test them only on the binary classification datasets
In the original M3GP algorithm, the fitness was the overall accuracy of the Mahalanobis distance classifier, but in the current implementation, we use the weighted average of F-measures (WAF) instead of the overall accuracy, for its robustness to class imbalance, especially in multiclass classification
Summary
Since the establishment of the Warsaw Framework in 2013, remote sensing (RS) is recommended as an appropriate technology for monitoring and measuring, reporting, and verification (MRV) for countries reporting forest land cover and land cover change to the UNFCCC (https://unfccc.int/topics/land-use/resources/warsaw-framework-for-reddplus (accessed on 15 November 2020)). Many difficulties, from the availability of adequate in situ reference data to the spatial and temporal resolution of freely available satellite imagery and data processing power, have been hindering the operational use of this technology for MRV. Spectral indices are combinations of reflectance values from different wavelengths that represent the relative abundance of certain terrain elements. They have been used by the RS community for a long time to enhance the identification of vegetation (e.g., NDVI [1]), water (e.g., NDWI [2]), burnt areas
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