# Random Ferns for Semantic Segmentation of PolSAR Images

arXiv: https://arxiv.org/abs/2202.03498 · HTML: https://mlsanity.com/paper/2202.03498/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 69 references that are in the map.
- First posted on arXiv 7 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks](https://mlsanity.com/paper/1711.08681/) (2017)
- [Deep Learning Meets SAR](https://mlsanity.com/paper/2006.10027/) (2020)
- [SpaceNet 6: Multi-Sensor All Weather Mapping Dataset](https://mlsanity.com/paper/2004.06500/) (2020)
- [Deep Forest](https://mlsanity.com/paper/1702.08835/) (2017)
- [Cell segmentation with random ferns and graph-cuts](https://mlsanity.com/paper/1602.05439/) (2016)

## Built on by (most cited first, 1 of 1)

- [SBSS: Stacking-Based Semantic Segmentation Framework for Very High Resolution Remote Sensing Image](https://mlsanity.com/paper/2212.07623/) (2022)

## Abstract (arXiv)

Random Ferns -- as a less known example of Ensemble Learning -- have been successfully applied in many Computer Vision applications ranging from keypoint matching to object detection. This paper extends the Random Fern framework to the semantic segmentation of polarimetric synthetic aperture radar images. By using internal projections that are defined over the space of Hermitian matrices, the proposed classifier can be directly applied to the polarimetric covariance matrices without the need to explicitly compute predefined image features. Furthermore, two distinct optimization strategies are proposed: The first based on pre-selection and grouping of internal binary features before the creation of the classifier; and the second based on iteratively improving the properties of a given Random Fern. Both strategies are able to boost the performance by filtering features that are either redundant or have a low information content and by grouping correlated features to best fulfill the independence assumptions made by the Random Fern classifier. Experiments show that results can be achieved that are similar to a more complex Random Forest model and competitive to a deep learning baseline.