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Remote sensing data acquired from various sensors have been used for decades to monitor sea ice conditions over polar regions. Sea ice plays an essential role in the polar environment and climate. Furthermore, sea ice affects anthropogenic activities, including shipping and navigation, the oil and gas industry, fisheries, tourism, and the lifestyle of the indigenous population of the Arctic. With the continuous decline of sea ice in the Arctic the presence of human-based activities will grow. Therefore, reliable information about sea ice conditions is of primary interest to protect the Arctic and to ensure safe and effective commercial activities and polar navigation. Currently, sea ice services produce operational ice charts manually using the knowledge of sea ice experts. However, with an increasing number of various data sources that provide different information regarding sea ice, it is important to develop automatic methods for sea ice characterization. Robust and automatic ice charting can not be achieved using only one satellite mission. It is fundamental to combine information from various remote sensors with different characteristics for more reliable sea ice monitoring and characterization. However, how do we know that all the information is actually relevant? It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms from the required processing time and accuracy point of view. Therefore, it is crucial to select an optimal set of features that provides the relevant information content to enhance the efficiency and accuracy of the image interpretation and retrieval of geophysical parameters. The work in this dissertation specifically focuses on the development of such a method. In this thesis, we employ a fully automatic, flexible, accurate, efficient, and interpretable information selection method that is based on the graph Laplacians. The proposed approach assesses relevant information on a global and local level using two metrics simultaneously and selects relevant features for different regions of an image according to their physical characteristics and observation conditions. Moreover, it is linked in a common scheme with a classification algorithm that helps to properly evaluate the performance of the information selection and provides sea ice classification maps as an output. Accordingly, in recent studies, we investigate and evaluate the robustness and effectiveness of the proposed method for sea ice classification by testing several data combinations with various sea ice conditions. Experiments illustrate the flexibility and efficiency of the proposed scheme and clearly indicate an advantage of combining various sensors. Moreover, the results demonstrate the potential for operational sea ice monitoring that should be further thoroughly examined in future studies.

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. Second, real-world data can show both homophily ('birds of a feather flock together') and heterophily ('opposites attract') during propagation, while standard algorithms only consider homophily. Both shortcomings are addressed in this work and the result is a graph-based label propagation algorithm for multimodal data that includes homophily and/or heterophily. Furthermore, the method is also able to transfer information between uni- and multimodal data. Experiments on the remote sensing data set of Houston, which contains a LiDAR and a hyperspectral image, show that our approach ties state-of-the-art methods for classification with an OA of 91.4%, while being more flexible and not constrained to a specific data set or a specific combination of modalities.