Dinh Ho Tong Minh

TL;DR: A better understanding of the capabilities of Sentinel-1 radar images for agricultural land cover mapping through the use of deep learning techniques is provided and it is found that in the near future these RNN-based techniques will play an important role in the analysis of remote sensing time series.

TL;DR: This study presents an attempt to overcome the issues outlined above based on direct 3-D imaging of the forest volume, which is possible through multibaseline SAR tomography, and the relevance of tomographic technique in P-band spaceborne mission is discussed.

TL;DR: A deep learning architecture to combine information coming from S1 and S2 time series, namely TWINNS (TWIn Neural Networks for Sentinel data), able to discover spatial and temporal dependencies in both types of SITS is proposed.

TL;DR: The European Space Agency's 7th Earth Explorer mission, BIOMASS, is to determine the worldwide distribution of forest above-ground biomass (AGB) in order to reduce the major uncertainties in calculations of carbon stocks and fluxes associated with the terrestrial biosphere, including carbon fluxe associated with Land Use Change, forest degradation and forest regrowth as mentioned in this paper.

TL;DR: In this article, a method to retrieve the top-of-canopy height from TomoSAR data was proposed, with an error of only 10% at 1.5-ha resolution.