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- W4308521184 abstract "AbstractDenoising images is a key part of the process of images. The hyperspectral image (HSI) has three dimensions in addition to the natural 2D image to display spectral and spatial information. In forestry [2, 2], agriculture, and urban planning, HSIs are commonly used. However, there are impacts with the multi-detector utilized to create the HSIs because of the harsh space environment, resulting in the noise of HSIs. The accuracy of consequent work, such as classification tasks [3], will be affected by the corrupted hyperspectral data with noise. As a result, in recent years have been an increase in interest in HSI denoising [4, 5]. Numerous methods, for example the Tenser-SVD [6] as well as the K-singular value decomposition [7], have been proposed. In general, the HSI denoising techniques may be categorized into three major groups." @default.
- W4308521184 created "2022-11-12" @default.
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- W4308521184 date "2022-11-09" @default.
- W4308521184 modified "2023-10-16" @default.
- W4308521184 title "Architecture Design for Analyzing Hyperspectral Images" @default.
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- W4308521184 doi "https://doi.org/10.1007/978-3-031-16868-0_12" @default.
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