Semi-Supervised Multi-Label Classification of Land Use/Land Cover in Remote Sensing Images With Predictive Clustering Trees and Ensembles

Abstract

The task of remote sensing image (RSI) classification has been studied extensively in the geoscience and remote sensing (RS) community. While deep learning methods have shown great success in solving this task, their reliance on large-scale labeled datasets is a serious limitation when dealing with complex labels and multiple semantic categories. The process of annotating such datasets can be time-consuming and tedious, leading to limited availability of labeled data and reduced performance of supervised learning methods. To address this issue, semi-supervised learning (SSL) methods can be applied, as they use both the limited labeled data and the abundant unlabeled data. In this article, we propose an effective SSL framework for RSI classification, which combines two key concepts. First, we employ a deep convolutional feature extractor to learn feature representations that encode the images into a lower dimensional feature space, capturing the rich semantic context present in RSI. Second, we utilize semi-supervised predictive clustering trees (PCTs) and ensembles thereof to learn from both the labeled and unlabeled data. To evaluate the effectiveness of the proposed framework, we compare it against several state-of-the-art self-supervised and semi-supervised methods from the literature. We conduct extensive experiments on ten publicly available land use/land cover RSI classification datasets: five for multiclass classification (MCC) and five for multi-label classification (MLC). The results demonstrate that the proposed framework has superior predictive performance compared to state-of-the-art methods from the literature, highlighting its effectiveness in semi-supervised RSI classification.

Topics & Concepts