274 research outputs found
Learnable Reconstruction Methods from RGB Images to Hyperspectral Imaging: A Survey
Hyperspectral imaging enables versatile applications due to its competence in
capturing abundant spatial and spectral information, which are crucial for
identifying substances. However, the devices for acquiring hyperspectral images
are expensive and complicated. Therefore, many alternative spectral imaging
methods have been proposed by directly reconstructing the hyperspectral
information from lower-cost, more available RGB images. We present a thorough
investigation of these state-of-the-art spectral reconstruction methods from
the widespread RGB images. A systematic study and comparison of more than 25
methods has revealed that most of the data-driven deep learning methods are
superior to prior-based methods in terms of reconstruction accuracy and quality
despite lower speeds. This comprehensive review can serve as a fruitful
reference source for peer researchers, thus further inspiring future
development directions in related domains
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
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