Journal Article10.1080/15481603.2017.1408892
Less is more: optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application
Stefanos Georganos,Taïs Grippa,Sabine Vanhuysse,Moritz Lennert,Michal Shimoni,Stamatis Kalogirou,Eléonore Wolff +6 more
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TL;DR: A new metric to perform model selection named classification optimization score (COS) that rewards model simplicity and indirectly penalizes for increased computational time and processing requirements using the number of features for a given classification model as a surrogate is proposed.
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Abstract: This study evaluates the impact of four feature selection (FS) algorithms in an object-based image analysis framework for very-high-resolution land use-land cover classification. The selected FS al...
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Citations
Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling
Stefanos Georganos,Taïs Grippa,Assane Niang Gadiaga,Catherine Linard,Moritz Lennert,Sabine Vanhuysse,Nicholus Mboga,Eléonore Wolff,Stamatis Kalogirou +8 more
TL;DR: From the first empirical results, it is concluded that GRF can be more predictive when an appropriate spatial scale is selected to model the data, with reduced residual autocorrelation and lower Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values.
304
Spatio-Temporal Patterns of Land Use/Land Cover Change in the Heterogeneous Coastal Region of Bangladesh between 1990 and 2017
Abu Yousuf Md Abdullah,Arif Masrur,Mohammed Sarfaraz Gani Adnan,Md. Abdullah Al Baky,Quazi K. Hassan,Ashraf Dewan +5 more
TL;DR: It is shown that the XGBoost feature selection approach effectively addresses the issue of high landscape heterogeneity and spectral complexities in the image data, successfully augmenting the RF model performance (providing a mean user’s accuracy > 0.82).
Very High Resolution Object-Based Land Use–Land Cover Urban Classification Using Extreme Gradient Boosting
Stefanos Georganos,Taïs Grippa,Sabine Vanhuysse,Moritz Lennert,Michal Shimoni,Eléonore Wolff +5 more
TL;DR: The results demonstrate that Xgboost parameterized with a Bayesian procedure, systematically outperformed RF and SVM, mainly in larger sample sizes.
Forest stand species mapping using the Sentinel-2 time series.
TL;DR: Using the Sentinel-2 time series instead of single date imagery significantly improved forest tree species mapping, by approximately 5–10% of overall accuracy, and combining images from spring and autumn resulted in better species discrimination.
228
References
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Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
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