Search Results for author:
Found 10 papers, 9 papers with code
Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring
Our application problem is the monitoring of lake ice on Alpine lakes.
Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery
From the ice maps, we derive long-term LIP trends.
Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams using Machine Learning
and generalisation scores of 71% (approx.)
Integrated monitoring of ice in selected Swiss lakes. Final project report
Secondly, and as a pilot project, the use of existing public Webcams was investigated for (a) validation of results from satellite data, and (b) independent estimation of lake ice, especially for small lakes like St. Moritz, that could not be possibly monitored in the satellite images.
Photi-LakeIce Dataset
On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work.
Change detection for remote sensing images
Image Segmentation
+4
Lake Ice Monitoring with Webcams and Crowd-Sourced Images
On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work.
Change detection for remote sensing images
Image Segmentation
+4
Lake Ice Detection from Sentinel-1 SAR with Deep Learning
Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming.
Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network
The aim of this research is to super-resolve the lower-resolution (20 m and 60 m Ground Sampling Distance - GSD) bands to 10 m GSD, so as to obtain a complete data cube at the maximal sensor resolution.
Learned Spectral Super-Resolution
We describe a novel method for blind, single-image spectral super-resolution.
Hyperspectral Super-Resolution by Coupled Spectral Unmixing
Hyperspectral super-resolution addresses this problem, by fusing a low-resolution hyperspectral image and a conventional high-resolution image into a product of both high spatial and high spectral resolution.
