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Change detection methods for remote sensing in the last decade: A comprehensive review
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 …
detect and analyze changes occurring in the same geographical area over time, which has …
A systematic literature review on multimodal machine learning: Applications, challenges, gaps and future directions
Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …
heterogeneous data from multiple modalities and machine learning (ML) are combined to …
Unsupervised domain adaptation augmented by mutually boosted attention for semantic segmentation of VHR remote sensing images
This work investigates unsupervised domain adaptation (UDA)-based semantic
segmentation of very high-resolution (VHR) remote sensing (RS) images from different …
segmentation of very high-resolution (VHR) remote sensing (RS) images from different …
A decoder-focused multitask network for semantic change detection
Z Li, X Wang, S Fang, J Zhao… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Recently, semantic change detection (SCD) has gained growing attention from the remote-
sensing (RS) research community due to its critical role in Earth observation applications …
sensing (RS) research community due to its critical role in Earth observation applications …
Beyond supervised learning in remote sensing: A systematic review of deep learning approaches
An increasing availability of remote sensing data in the era of geo big-data makes producing
well-represented, reliable training data to be more challenging and requires an excessive …
well-represented, reliable training data to be more challenging and requires an excessive …