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- W4207039757 abstract "Tensor representation is a feasible solution for analyzing large-volume, multirelational, and multimodal datasets, which are often conveniently represented as multiway arrays or tensors. It is therefore valuable and promising for the geoscience and remote sensing research communities to review tensor representation as an emerging tool for remote sensing data analysis. This chapter presents some relevant remote sensing data analysis methodologies and techniques, organized in two main topics: data fusion and feature extraction. To start with, we provide brief reviews on the mechanisms and conventional methods of both topics. We then introduce several methods that have achieved state-of-the-art performances by incorporating tensor representation into the models. Furthermore, three tensor representation-based methods are discussed in detail to demonstrate the superiority of tensor representation in remote sensing image processing." @default.
- W4207039757 created "2022-01-26" @default.
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- W4207039757 date "2022-01-01" @default.
- W4207039757 modified "2023-09-30" @default.
- W4207039757 title "Tensor representation for remote sensing images" @default.
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- W4207039757 doi "https://doi.org/10.1016/b978-0-12-824447-0.00019-4" @default.
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