SAR

Detection of Forest Fires through Deep Unsupervised Learning Modeling of Sentinel-1 Time Series

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 …

Grad-SLAM: Explaining Convolutional Autoencoders’ Latent Space of Satellite Image Time Series

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 …

FARMSAR: Fixing AgRicultural Mislabels Using Sentinel-1 Time Series and AutoencodeRs

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 …

EUSAR 2022: 14th European Conference on Synthetic Aperture Radar

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 …

Modelling of agricultural SAR Time Series using Convolutional Autoencoder for the extraction of harvesting practices of rice fields

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 …

CSRS 2022: Canadian Symposium on Remote Sensing

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 …

Extracting relevance from SAR temporal profiles on a glacier and an alpine watershed by a deep autoencoder

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 …

ISPRS CONGRESS 2022: International Society for Photogrammetry and Remote Sensing

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 …

LPS 2022: Living Planet Symposium

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 …

Beets or Cotton? Blind Extraction of Fine Agricultural Classes Using a Convolutional Autoencoder Applied to Temporal SAR Signatures

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 …