With an increase in the amount of natural disasters, the combined use of cloud-penetrating Synthetic Aperture Radar and deep learning becomes unavoidable for their monitoring. This article proposes a methodology for forest fire detection using …
This paper introduces a tool for explaining the latent space generated by applying convolutional autoencoders to satellite image time series, entitled Grad-SLAM. We rely on backpropagated gradient interpretation combined with network activation …
This paper aims to quantify the errors in the provided agricultural crop types, estimate the possible error rate in the available dataset, and propose a correction strategy. This quantification could establish a confidence criterion useful for …
We apply an unsupervised learning methodology to project SAR Time Series of growing rice fields onto a 3-dimensional space, where we explicit differences between the fields. The projection method used is a Convolutional Autoencoder, trained using a …
We apply an unsupervised learning methodology to project SAR Time Series of growing rice fields onto a 3-dimensional space, where we explicit differences between the fields. The projection method used is a Convolutional Autoencoder, trained using a …
The analysis of C-band SAR backscatter time-series over boreal forests can provide tools for a variety of forestry-related applications, such as the estimation of biophysical attributes (Antropov & Rauste & Häme & Praks, 2017) or the assessment of …
This paper proposes to use methods for compressing the temporal profiles of Sentinel-1 images, in order to be able to evaluate and analyze the richness of the temporal dynamics, both on a glacier and on a watershed. We propose to use unsupervised …
This paper proposes to use methods for compressing the temporal profiles of Sentinel-1 images, in order to be able to evaluate and analyze the richness of the temporal dynamics, both on a glacier and on a watershed. We propose to use unsupervised …
Presentations of 2 posters at LPS 2022 on various applications of Convolutional Autoencoders to SAR time series:
- Convolutional Autoencoder for the unsupervised extraction of fire footprints from Sentinel-1 time-series
- Extraction of variations in …
We present a fully unsupervised learning pipeline, which involves both a projection method and a clustering algorithm dedicated to the pixel-wise classification of multitemporal SAR images. We design a Convolutional Autoencoder as the method to …