Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion ...
Land cover datasets are essential for modeling Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, and finding quality satellite remote sensing datasets...
| Main Authors: | , , |
|---|---|
| Format: | Audio |
| Language: | English |
| Published: |
Friedrich-Schiller-Universität Jena
2018
|
| Subjects: | |
| Online Access: | https://dx.doi.org/10.22032/dbt.37838 https://www.db-thueringen.de/receive/dbt_mods_00037838 |
| Summary: | Land cover datasets are essential for modeling Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, and finding quality satellite remote sensing datasets to produce such maps is difficult due to cloud cover, polar darkness, and poor availability of high-resolution imagery. A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5 m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. The datasets from hyperspectral, multispectral, synthetic aperture radar (SAR) platforms, and terrain datasets were fused together using unsupervised and supervised classification techniques over a 343 km2 region to generate high-resolution (5 m) vegetation type maps. A unsupervised technique was developed to classify high-dimensional remote sensing datasets into cohesive clusters and a quantitative technique ... : ICEI 2018 : 10th International Conference on Ecological Informatics- Translating Ecological Data into Knowledge and Decisions in a Rapidly Changing World ... |
|---|