4.7 Article

Semi-Supervised Learning for Joint SAR and Multispectral Land Cover Classification

Related references

Note: Only part of the references are listed.
Article Geochemistry & Geophysics

Remote Sensing Image Scene Classification With Self-Supervised Paradigm Under Limited Labeled Samples

Chao Tao et al.

Summary: With the development of deep learning, supervised learning methods have shown good performance in remote sensing image scene classification. However, these methods require a large amount of labeled data for training. In this study, a new self-supervised learning mechanism is introduced to obtain high-performance pretraining models for scene classification from large unlabeled data. Experiments demonstrate that this new paradigm outperforms traditional ImageNet pretrained models, and the insights obtained can contribute to the development of self-supervised learning in the remote sensing community.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

Article Geochemistry & Geophysics

Self-Supervised SAR-Optical Data Fusion of Sentinel-1/-2 Images

Yuxing Chen et al.

Summary: The study introduces a self-supervised framework for SAR-optical data fusion and land-cover mapping tasks, achieving comparable accuracy with image-level contrastive learning by fusing SAR and optical images through multi-view contrastive loss, and combining pretrained features and spectral information to assign land-cover classes to each pixel.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

Article Computer Science, Artificial Intelligence

Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey

Longlong Jing et al.

Summary: This paper reviews deep learning-based self-supervised general visual feature learning methods, covering motivation, pipeline, architectures, schema, evaluation metrics, datasets, performance comparisons, and future directions.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

Article Geochemistry & Geophysics

Deep Unsupervised Embedding for Remotely Sensed Images Based on Spatially Augmented Momentum Contrast

Jian Kang et al.

Summary: The article presents a new unsupervised deep metric learning model called SauMoCo, designed to characterize unlabeled RS scenes by defining spatial augmentation criteria and constructing a queue of deep embeddings. The proposed approach substantially enhances the discrimination ability among complex land cover categories of RS tiles.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

Article Engineering, Electrical & Electronic

Global Land-Cover Mapping With Weak Supervision: Outcome of the 2020 IEEE GRSS Data Fusion Contest

Caleb Robinson et al.

Summary: This article presents the scientific outcomes of the 2020 Data Fusion Contest (DFC2020) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society, addressing the problem of automatic global land-cover mapping with weak supervision. The results of the best-performing methods during the contest are reported, along with a description of the DFC2020 dataset available for further evaluation.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

Review Engineering, Multidisciplinary

A Survey on Contrastive Self-Supervised Learning

Ashish Jaiswal et al.

Summary: Self-supervised learning, particularly through contrastive learning, has gained popularity for its cost-effective approach in using self-defined pseudolabels for various downstream tasks. This paper extensively reviews self-supervised methods following the contrastive approach, explaining pretext tasks and different architectures used. Performance comparisons across multiple downstream tasks demonstrate variations in method effectiveness.

TECHNOLOGIES (2021)