It is of considerable benefit to combine information obtained from different satellite sensors to achieve advanced and improved characterization of sea ice conditions. However, it is also true that not all the information is relevant. It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms. Therefore, it is crucial to select an optimal ...

When dealing with multivariate remotely sensed records collected by multiple sensors, an accurate selection of information at the data, feature, or decision level is instrumental in improving the scenes’ characterization. This will also enhance the system’s efficiency and provide more details on modeling the physical phenomena occurring on the Earth’s surface. In this article, we introduce a flexible ...

The field of Earth observation is dealing with increasingly large, multimodal data sets. An important processing step consists of providing these data sets with labels. However, standard label propagation algorithms cannot be applied to multimodal remote sensing data for two reasons. First, multimodal data is heterogeneous while classic label propagation algorithms assume a homogeneous network. ...

Automatic ice charting cannot be achieved using only SAR modalities. It is fundamental to combine information from other remote sensors with different characteristics for more reliable sea ice characterization. In this paper, we employ principal feature analysis (PFA) to select significant information from multimodal remote sensing data. PFA is a simple yet very effective approach that can be applied ...