Random Ferns for Semantic Segmentation of PolSAR Images

TL;DR

This paper adapts Random Ferns, an ensemble learning method, for semantic segmentation of PolSAR images by leveraging internal projections over Hermitian matrices, achieving competitive results with deep learning.

Contribution

It introduces a novel application of Random Ferns to PolSAR image segmentation and proposes two optimization strategies to enhance performance without explicit feature computation.

Findings

Achieves segmentation accuracy comparable to Random Forests.

Performs competitively against deep learning baselines.

Enhances Random Ferns with feature filtering and grouping strategies.

Abstract

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.