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 …
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 …