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Transformers in Remote Sensing: A Survey [PDF]
arXiv, 2022 Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformers-based architectures, originally introduced in natural language processing, have pervaded computer vision field where the self-attention mechanism has been utilized as a replacement to the popular
Abdulaziz Amer Aleissaee +6 more
arxiv +3 more sources
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data [PDF]
arXiv, 2021 Remote sensing and automatic earth monitoring are key to solve global-scale challenges such as disaster prevention, land use monitoring, or tackling climate change. Although there exist vast amounts of remote sensing data, most of it remains unlabeled and thus inaccessible for supervised learning algorithms.
Oscar Mañas +4 more
arxiv +3 more sources
Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and
Remote Sensing, 13: 3735-3756, 2020, 2020 Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn ...
Gong Cheng +4 more
arxiv +3 more sources
SpectralGPT: Spectral Remote Sensing Foundation Model [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner.
D. Hong +13 more
semanticscholar +1 more source
RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation Based on Visual Foundation Model [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2023 Leveraging the extensive training data from SA-1B, the segment anything model (SAM) demonstrates remarkable generalization and zero-shot capabilities.
Keyan Chen +6 more
semanticscholar +1 more source
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2023 General-purpose foundation models have led to recent breakthroughs in artificial intelligence (AI). In remote sensing, self-supervised learning (SSL) and masked image modeling (MIM) have been adopted to build foundation models.
F. Liu +5 more
semanticscholar +1 more source
Remote Sensing Image Change Detection With Transformers [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2021 Modern change detection (CD) has achieved remarkable success by the powerful discriminative ability of deep convolutions. However, high-resolution remote sensing CD remains challenging due to the complexity of objects in the scene.
Hao Chen, Zipeng Qi, Zhenwei Shi
semanticscholar +1 more source
Remote Sensing, 2022 In the original article [...]
Remote Sensing Editorial Office
doaj +1 more source
Acknowledgment to Reviewers of Remote Sensing in 2021
Remote Sensing, 2022 Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Remote Sensing Editorial Office
doaj +1 more source
Editorial: Remote Sensing for Aquaculture [PDF]
Frontiers in Marine Science, 2021 International ...
Gernez, Pierre +3 more
openaire +10 more sources