Targeted Land Cover Classification

Abstract

This paper addresses a specific typology of land cover classification problems, hereinafter referred to as “targeted land cover classification”, where the objective is the identification of only one or few specific “targeted” land cover classes of interest, disregarding all the other potential classes present in the area under analysis. Such a challenging problem, which is common to a variety of operational information services and applications (e.g., agriculture, forestry, spatial planning, ecosystem monitoring, disaster management, habitat mapping, etc.), can be effectively solved by traditional supervised classification techniques provided that an exhaustive ground truth is available for all the land cover classes present in the region of interest. Such a requirement is seldom satisfied and presents several practical drawbacks and limitations, both in terms of time and economic cost that may render this task difficult to achieve in most real-life cases. However, the possibility to perform an effective targeted classification using only ground-truth samples for the class(es) of interest (hence avoiding the burden and cost associated with the collection of a full and exhaustive ground-truth information) would represent a significant advantage. In this paper, we present a novel technique capable of identifying specific land cover classes of interest by exploiting the ground truth only available for these targeted classes, while providing accuracies comparable to those of traditional fully-supervised methods. The proposed technique jointly exploits both the unlabeled samples of the image under investigation and the training samples only available for the targeted classes. In particular, the Expectation-Maximization (EM) algorithm and Markov random fields (MRF) are employed to estimate the probability density function of both the class(es) of interest and the unknown class representing the merger of all the unknown land cover classes characterizing the study area for which no ground-truth information is available. An extensive experimental analysis and cross-comparisons with both fully-supervised support vector machines (SVM) and ensembles of multiple one-class support vector data description (SVDD) classifiers on different datasets confirmed the effectiveness and the reliability of the proposed technique.