Interpretable Hyperspectral AI: When Non-Convex Modeling meets
Hyperspectral Remote Sensing
- URL: http://arxiv.org/abs/2103.01449v1
- Date: Tue, 2 Mar 2021 03:32:10 GMT
- Title: Interpretable Hyperspectral AI: When Non-Convex Modeling meets
Hyperspectral Remote Sensing
- Authors: Danfeng Hong and Wei He and Naoto Yokoya and Jing Yao and Lianru Gao
and Liangpei Zhang and Jocelyn Chanussot and Xiao Xiang Zhu
- Abstract summary: Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience remote sensing (RS)
In the past decade efforts have been made to process analyze these hyperspectral (HS) products mainly by means of seasoned experts.
For this reason, it is urgent to develop more intelligent and automatic approaches for various HS RS applications.
- Score: 57.52865154829273
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Hyperspectral imaging, also known as image spectrometry, is a landmark
technique in geoscience and remote sensing (RS). In the past decade, enormous
efforts have been made to process and analyze these hyperspectral (HS) products
mainly by means of seasoned experts. However, with the ever-growing volume of
data, the bulk of costs in manpower and material resources poses new challenges
on reducing the burden of manual labor and improving efficiency. For this
reason, it is, therefore, urgent to develop more intelligent and automatic
approaches for various HS RS applications. Machine learning (ML) tools with
convex optimization have successfully undertaken the tasks of numerous
artificial intelligence (AI)-related applications. However, their ability in
handling complex practical problems remains limited, particularly for HS data,
due to the effects of various spectral variabilities in the process of HS
imaging and the complexity and redundancy of higher dimensional HS signals.
Compared to the convex models, non-convex modeling, which is capable of
characterizing more complex real scenes and providing the model
interpretability technically and theoretically, has been proven to be a
feasible solution to reduce the gap between challenging HS vision tasks and
currently advanced intelligent data processing models.
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