Semi-supervised learning (SSL) has made significant strides in the field of remote sensing.
Finding a large number of labeled datasets for SSL methods is uncommon, and manually …

Semisupervised learning (SSL) techniques explore the progressive discovery of the hidden
latent data structure by propagating supervised information on unlabeled data, which are …

Land cover classification has significant contributions in several applications including
natural calamities estimation and response, observation of environmental changes, and …

Semi-supervised learning has been well developed to help reduce the cost of manual
labelling by exploiting a large quantity of unlabelled data. Especially in the application of …

Collecting large annotated datasets in Remote Sensing is often expensive and thus can
become a significant obstacle for training advanced machine learning models. Standard …

Semi-supervised learning (SSL), which aims to learn with limited labeled data and massive
amounts of unlabeled data, offers a promising approach to exploit the massive amounts of …

Deep semi-supervised learning (DSSL) is a rapidly-growing field that takes advantage of a
limited number of labeled examples to leverage massive amounts of unlabeled data. The …

Accurate classification of land cover from aerial images is one of the research topics in
remote sensing and is also in high demand in industry. However, obtaining labeled data for …

Semisupervised learning techniques are gaining popularity due to their capability of building
models that are effective, even when scarce amounts of labeled data are available. In this …

This work develops robust semi-supervised classifiers to tackle three most challenging
problems in land-use classification using remote sensing data, namely mixed pixels, weak …