Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory

TL;DR

This paper introduces a multi-stage fuzzy rule-based classifier that leverages evidence theory and spatial context aggregation to improve land cover classification accuracy in multispectral satellite images.

Contribution

It presents novel aggregation methods using Dempster-Shafer theory and fuzzy k-NN for contextual classification, outperforming traditional MRF-based approaches.

Findings

Proposed methods outperform MRF-based classification.

Achieved satisfactory accuracy on benchmark satellite images.

Utilized evidence theory for effective spatial information integration.

Abstract

Land cover classification using multispectral satellite image is a very challenging task with numerous practical applications. We propose a multi-stage classifier that involves fuzzy rule extraction from the training data and then generation of a possibilistic label vector for each pixel using the fuzzy rule base. To exploit the spatial correlation of land cover types we propose four different information aggregation methods which use the possibilistic class label of a pixel and those of its eight spatial neighbors for making the final classification decision. Three of the aggregation methods use Dempster-Shafer theory of evidence while the remaining one is modeled after the fuzzy k-NN rule. The proposed methods are tested with two benchmark seven channel satellite images and the results are found to be quite satisfactory. They are also compared with a Markov random field (MRF) model-based contextual classification method and found to perform consistently better.