Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery
Fulgencio Cánovas-García,Fulgencio Cánovas-García,Francisco Alonso-Sarría,Francisco Gomariz-Castillo,Fernando Oñate-Valdivieso +4 more
80
TL;DR: It is shown that out-of-bag cross-validation clearly overestimates accuracy, both overall and per class when classifying remote sensing imagery using training areas with several pixels or objects, and a modification of the random forest algorithm to split training patches instead of the pixels (or objects) that compose them is proposed.
read more
About: This article is published in Computers & Geosciences. The article was published on 01 Jun 2017. and is currently open access. The article focuses on the topics: Random forest & Pixel.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Implementation of machine-learning classification in remote sensing: an applied review
TL;DR: An overview of machine learning from an applied perspective focuses on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and k-nearest neighbours (k-NN).
1.5K
Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition
TL;DR: The results indicate that temporal aggregation (e.g., median) is a promising method, which not only significantly reduces data volume (resulting in an easier and faster analysis) but also produces an equally high accuracy as time series data.
421
Importance of spatial predictor variable selection in machine learning applications -- Moving from data reproduction to spatial prediction
TL;DR: It is confirmed that spatial cross-validation is essential in preventing overoptimistic model performance and that in addition to spatial validation, a spatial variable selection must be considered in spatial predictions of ecological data to produce reliable predictions.
334
Cascaded Random Forest for Hyperspectral Image Classification
TL;DR: A Cascaded Random Forest method, which can improve the classification performance by means of combining two different enhancements into the Random Forest (RF) algorithm, with out-of-bag error added to update the sample weights in CRF.
102
Imputation of missing well log data by random forest and its uncertainty analysis
TL;DR: Well log data from the Volve Field are used for validation of the prediction obtained by random forest, in which a high correlation coefficient between prediction and reference is achieved.
82
References
Random Forests
Leo Breiman
- 01 Oct 2001
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Textural Features for Image Classification
Robert M. Haralick,K. Shanmugam,Its'hak Dinstein +2 more
- 01 Nov 1973
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
23.6K
•Book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor Hastie,Robert Tibshirani,Jerome H. Friedman +2 more
- 28 Jul 2013
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
21.3K
Classification and Regression by randomForest
Andy Liaw,Matthew C. Wiener +1 more
- 01 Jan 2007
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.