On the Exploitation of Heterophily in Graph-Based Multimodal Remote Sensing Data Analysis

Source at https://ceur-ws.org/.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

A dataset of direct observations of sea ice drift and waves in ice

Variability in sea ice conditions, combined with strong couplings to the atmosphere and the ocean, lead to a broad range of complex sea ice dynamics. More in-situ measurements are needed to better identify the phenomena and mechanisms that govern sea ice growth, drift, and breakup. To this end, we have gathered a dataset of in-situ observations of sea ice drift and waves in ice. A total of 15 deployments were performed over a period of 5 years in both the Arctic and Antarctic, involving 72 instruments. These provide both GPS drift tracks, and measurements of waves in ice. The data can, in turn, be used for tuning sea ice drift models, investigating waves damping by sea ice, and helping calibrate other sea ice measurement techniques, such as satellite based observations

On the Exploitation of Heterophily in Graph-Based Multimodal Remote Sensing Data Analysis

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