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Found 7 papers, 4 papers with code
SpectralEarth: Training Hyperspectral Foundation Models at Scale
To close this gap, we introduce SpectralEarth, a large-scale multi-temporal dataset designed to pretrain hyperspectral foundation models leveraging data from the Environmental Mapping and Analysis Program (EnMAP).
Decoupling Common and Unique Representations for Multimodal Self-supervised Learning
The increasing availability of multi-sensor data sparks wide interest in multimodal self-supervised learning.
Semi-Supervised Learning for hyperspectral images by non parametrically predicting view assignment
Recently, to effectively train the deep learning models with minimal labelled samples, the unlabeled samples are also being leveraged in self-supervised and semi-supervised setting.
SSL4EO-L: Datasets and Foundation Models for Landsat Imagery
The Landsat program is the longest-running Earth observation program in history, with 50+ years of data acquisition by 8 satellites.
SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
Self-supervised pre-training bears potential to generate expressive representations without human annotation.
Ranked #1 on
Multi-Label Image Classification
on BigEarthNet (official test set)
(using extra training data)
Self-supervised Learning in Remote Sensing: A Review
In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities.
Ranked #3 on
Multi-Label Image Classification
on BigEarthNet
Self Supervised Learning for Few Shot Hyperspectral Image Classification
Deep learning has proven to be a very effective approach for Hyperspectral Image (HSI) classification.
