Aug 8, 2016 · Abstract: Dimensionality reduction is one of the most important tasks in improving hyperspectral image (HSI) classification performance and ...
Dimensionality reduction is one of the most important tasks in improving hyperspectral image (HSI) classification performance and has been widely studied.
A group-based tensor model for HIS dimensionality reduction is proposed and the local and nonlocal spatial information of HSI cubes is explored by ...
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Hyperspectral image (HSI) classification requires spectral dimensionality reduction (DR) and spatial filtering. While common DR and denoising methods use ...
Jul 18, 2024 · This paper presents a sparse tensor based Support Tensor Machine (STM) HSI classification algorithm to solve this problem.
Apr 2, 2021 · Graph learning is an effective dimensionality reduction (DR) manner to analyze the intrinsic properties of high dimensional data, ...
May 6, 2017 · A group based tensor model [27] by exploiting clustering technique was developed for DR and classification. In addition, a tensor discriminative ...
Experimental results of three real-world hyperspectral datasets demonstrate that the proposed TSLGDA algorithm greatly improves the classification ...
In this paper, we propose to use both methods simultaneously for dimensionality reduction in order to obtain an accurate HSI classification.
Oct 12, 2024 · This model can acquire and utilize spectral information group-wise while incorporating a cross-layer transformer encoder. A positive feedback ...