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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
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GeoChat:Grounded Large Vision-Language Model for Remote Sensing [PDF]
Computer Vision and Pattern Recognition, 2023 Recent advancements in Large Vision-Language Models (VLMs) have shown great promise in natural image domains, allowing users to hold a dialogue about given visual content. However, such general-domain VLMs perform poorly for Remote Sensing (RS) scenarios,
Kartik Kuckreja +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
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
The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot [PDF]
International Journal of Applied Earth Observation and Geoinformation, 2023 Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentation model by Meta AI, in the field of remote sensing image analysis.
L. Osco +6 more
semanticscholar +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
Remote Sensing, 2022 In the original article [...]
Remote Sensing Editorial Office
doaj +1 more source
RSGPT: A Remote Sensing Vision Language Model and Benchmark [PDF]
Isprs Journal of Photogrammetry and Remote Sensing, 2023 The emergence of large-scale large language models, with GPT-4 as a prominent example, has significantly propelled the rapid advancement of artificial general intelligence and sparked the revolution of Artificial Intelligence 2.0.
Yuan Hu +4 more
semanticscholar +1 more source
Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review [PDF]
Remote Sensing, 2023 Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover
Guangliang Cheng +6 more
semanticscholar +1 more source