8,502 research outputs found
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution
Self-supervised cross-modal super-resolution (SR) can overcome the difficulty
of acquiring paired training data, but is challenging because only
low-resolution (LR) source and high-resolution (HR) guide images from different
modalities are available. Existing methods utilize pseudo or weak supervision
in LR space and thus deliver results that are blurry or not faithful to the
source modality. To address this issue, we present a mutual modulation SR
(MMSR) model, which tackles the task by a mutual modulation strategy, including
a source-to-guide modulation and a guide-to-source modulation. In these
modulations, we develop cross-domain adaptive filters to fully exploit
cross-modal spatial dependency and help induce the source to emulate the
resolution of the guide and induce the guide to mimic the modality
characteristics of the source. Moreover, we adopt a cycle consistency
constraint to train MMSR in a fully self-supervised manner. Experiments on
various tasks demonstrate the state-of-the-art performance of our MMSR.Comment: ECCV 202
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