Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features

By Peijun Du, A. Samat, Björn Waske, Sicong Liu, Zhenhong Li. 2015

429 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/06e63d8346c81ab0c716c1739580eecca9f3c55a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Mohammadreza Sheykhmousa
2. Saeid Homayouni
3. Masoud MahdianPari
4. Pedram Ghamisi
5. Fariba Mohammadimanesh
6. Hamid Ghanbari

In Media Technology

1. Masoud MahdianPari
2. Pedram Ghamisi
3. Fariba Mohammadimanesh
4. Yushi Chen
5. Jon Atli Benediktsson
6. Antonio J. Plaza

Citations in the corpus

• Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review (2020, background): “…classification[26]isalsoinfluencedbydifferentfactors,such asclassificationalgorithms[27]–[30],sensortypes[31],[32], trainingsamples[33]–[37],inputfeatures[38]–[41],pre-and postprocessingtechniques[42],[43],ancillarydata[44],target classes[22],[45],andtheaccuracyofthefinalproduct[44], [46]–[48].”