Multilabel Conditional Random Field Classification for UAV Images

Abstract

In this letter, we formulate the multilabeling classification problem of unmanned aerial vehicle (UAV) imagery within a conditional random field (CRF) framework with the aim of exploiting simultaneously spatial contextual information and cross-correlation between labels. The pipeline of the framework consists of two main phases. First, the considered input UAV image is subdivided into a grid of tiles, which are processed thanks to an opportune representation and a multilayer perceptron classifier providing thus tile-wise multilabel prediction probabilities. In the second phase, a multilabel CRF model is applied to integrate spatial correlation between adjacent tiles and the correlation between labels within the same tile, with the objective to improve iteratively the multilabel classification map associated with the considered input UAV image. Experimental results achieved on two different UAV image data sets are reported and discussed.