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Found 11 papers, 3 papers with code
Semi Supervised Heterogeneous Domain Adaptation via Disentanglement and Pseudo-Labelling
Such a setting is denoted as Semi-Supervised Heterogeneous Domain Adaptation (SSHDA) and it exhibits an even more severe distribution shift due to modality heterogeneity across domains. To cope with the challenging SSHDA setting, here we introduce SHeDD (Semi-supervised Heterogeneous Domain Adaptation via Disentanglement) an end-to-end neural framework tailored to learning a target domain classifier by leveraging both labelled and unlabelled data from heterogeneous data sources.
Reuse out-of-year data to enhance land cover mappingvia feature disentanglement and contrastive learning
Typically, when creating a land cover (LC) map, precise ground truth data is collected through time-consuming and expensive field campaigns.
Comparison of convolutional neural networks for cloudy optical images reconstruction from single or multitemporal joint SAR and optical images
With the increasing availability of optical and synthetic aperture radar (SAR) images thanks to the Sentinel constellation, and the explosion of deep learning, new methods have emerged in recent years to tackle the reconstruction of optical images that are impacted by clouds.
Attentive Weakly Supervised land cover mapping for object-based satellite image time series data with spatial interpretation
Nowadays, modern Earth Observation systems continuously collect massive amounts of satellite information.
Object-based multi-temporal and multi-source land cover mapping leveraging hierarchical class relationships
European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at highspatial resolution and high revisit time, respectively, radar and optical imagesthat support a wide range of Earth surface monitoring tasks such as LandUse/Land Cover mapping.
Supervised level-wise pretraining for recurrent neural network initialization in multi-class classification
To the best of our knowledge, despite the great interest in RNN-based classification, this is the first data-aware strategy dealing with the initialization of such models.
Combining Sentinel-1 and Sentinel-2 Time Series via RNN for object-based land cover classification
Radar and Optical Satellite Image Time Series (SITS) are sources of information that are commonly employed to monitor earth surfaces for tasks related to ecology, agriculture, mobility, land management planning and land cover monitoring.
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
In this work, we propose the first deep learning architecture for the analysis of SITS data, namely \method{} (DUal view Point deep Learning architecture for time series classificatiOn), that combines Convolutional and Recurrent neural networks to exploit their complementarity.
MRFusion: A Deep Learning architecture to fuse PAN and MS imagery for land cover mapping
Common techniques to produce land cover maps from such VHSR images typically opt for a prior pansharpening of the multi-resolution source for a full resolution processing.
Deep Recurrent Neural Networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1
The objective of this paper is to provide a better understanding of the capabilities of radar Sentinel-1 and deep learning concerning about mapping winter vegetation quality coverage.
Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks
Nowadays, modern earth observation programs produce huge volumes of satellite images time series (SITS) that can be useful to monitor geographical areas through time.
