Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas

2016 ◽  
Vol 187 ◽  
pp. 156-168 ◽  
Author(s):  
Charlotte Pelletier ◽  
Silvia Valero ◽  
Jordi Inglada ◽  
Nicolas Champion ◽  
Gérard Dedieu
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Random Forests ◽  
Satellite Image ◽  
High Resolution Satellite Image

scholarly journals Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series

2017 ◽  
Vol 9 (1) ◽  
pp. 95 ◽  
Author(s):  
Jordi Inglada ◽  
Arthur Vincent ◽  
Marcela Arias ◽  
Benjamin Tardy ◽  
David Morin ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Image ◽  
Land Cover Map

scholarly journals Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems

2019 ◽  
Author(s):  
Andrei Stoian ◽  
Vincent Poulain ◽  
Jordi Inglada ◽  
Victor Poughon ◽  
Dawa Derksen
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Satellite Image ◽  
Image Data ◽  
Design Decision ◽  
Land Cover Maps ◽  
Sentinel 2

The Sentinel-2 satellite mission offers high resolution multispectral time series image data, enabling the production of detailed land cover maps globally. At this scale, the trade-off between processing time and result quality is a central design decision. Currently, this machine learning task is usually performed using pixelwise classification methods. The radical shift of the computer vision field away from hand engineered image features and towards more automation by representation learning comes with many promises, including higher quality results and less engineering effort. In this paper we assess fully convolutional neural networks architectures as replacements for a Random Forest classifier in an operational context for the production of high resolution land cover maps with Sentinel-2 time series at the country scale. Our contributions include a framework for working with Sentinel-2 L2A time series image data, an adaptation of the U-Net model for dealing with sparse annotation data while maintaining high resolution output, and an analysis of those results in the context of operational production of land cover maps.

scholarly journals Development of segmentation algorithm based region growing for software GIS GRASS

2010 ◽  
Vol 58 (1) ◽  
pp. 207-216
Author(s):  
Aleš Tippner
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Land Cover ◽  
Mathematical Morphology ◽  
Satellite Images ◽  
Satellite Image ◽  
Region Growing ◽  
Segmentation Algorithm ◽  
Aerial Photographs ◽  
High Resolution Satellite Image

Image segmentation is fundamental prerequisite for new satellite images interpretation methods. GIS GRASS provides segmentation tools enabling global image segmentation only. We designed procedure enabling local segmentation using existing GRASS tools and segmentation algorithm based on region growing that we developed with C++. This algorithm applies mathematical morphology operators to output segments, too. Principial aim of the project is creation of useful input for differentiation of base land cover classes in panchromatic high-resolution satellite image (or historical aerial photographs for example).

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