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Found 11 papers, 10 papers with code
Deep Learning for Satellite Image Time Series Analysis: A Review
Earth observation (EO) satellite missions have been providing detailed images about the state of the Earth and its land cover for over 50 years.
Match-And-Deform: Time Series Domain Adaptation through Optimal Transport and Temporal Alignment
While large volumes of unlabeled data are usually available, associated labels are often scarce.
Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding
Unlike previous positional encoding based on calendar time (e. g. day-of-year), TPE is based on thermal time, which is obtained by accumulating daily average temperatures over the growing season.
TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation
However, when applied to target regions spatially different from the training region, these models perform poorly without any target labels due to the temporal shift of crop phenology between regions.
Elastic Similarity and Distance Measures for Multivariate Time Series
Elastic similarity and distance measures are a class of similarity measures that can compensate for misalignments in the time axis of time series data.
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping
In this paper we present Sourcerer, a Bayesian-inspired, deep learning-based, semi-supervised DA technique for producing land cover maps from SITS data.
InceptionTime: Finding AlexNet for Time Series Classification
TSC is the area of machine learning tasked with the categorization (or labelling) of time series.
TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification
We demonstrate that TS-CHIEF can be trained on 130k time series in 2 days, a data quantity that is beyond the reach of any TSC algorithm with comparable accuracy.
BreizhCrops: A Time Series Dataset for Crop Type Mapping
We present Breizhcrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series.
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
The experimental results show that TempCNNs are more accurate than RF and RNNs, that are the current state of the art for SITS classification.
Proximity Forest: An effective and scalable distance-based classifier for time series
We demonstrate on a 1M time series Earth observation dataset that Proximity Forest retains this accuracy on datasets that are many orders of magnitude greater than those in the UCR repository, while learning its models at least 100, 000 times faster than current state of the art models Elastic Ensemble and COTE.
