Class Information-based Principal Component Analysis Algorithm for Improved Hyperspectral Image Classification

Sunkara, Renuvenkataswamy; Singh, Arvind Kumar; Kadambi, Govind R.

doi:10.1109/migars57353.2023.10064597

Cited by 3 publications

(1 citation statement)

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“…Analyzing and processing the spectral and spatial characteristics of the various types of land features in the HSIs classify each image element into a category corresponding to the actual land features, thus enabling the classification of land features. Conventional methods generally use band selection and feature extraction for dimensionality reduction, compressing the original spectral image elements into a low-dimensional space, such as principal component analysis, 13 support vector machines (SVM), 14 and random forests. 15 The properties of HSIs constrain these methods, and their classification results could be better.…”

Section: Introductionmentioning

confidence: 99%

Spatially augmented guided sequence-based bidirectional encoder representation from transformer networks for hyperspectral classification studies

Zhang,

Bao,

Liang

et al. 2023

Opt. Eng.

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In recent years, bidirectional encoder representation from transformers (BERT) models have achieved superior performance in hyperspectral images (HSIs). It can capture the long-range correlations between HSI elements, but the local space and spectral band information of HSI is insufficient. We propose a spatially augmented guided sequence BERT network for HSI classification study, referred to as SAS-BERT, which makes more effective use of HSI's spatial and spectral information by improving the BERT model. First, a spatial augmentation learning module is added in the preprocessing stage to obtain more significant spatial features before the input network and better guide the spatial sequence. Then a spectral correlation module was used to represent the spectral band features of the HSI and to establish a correlation with the spatial location of the images to obtain better classification performance. Experimental results on three datasets show that the method proposed achieves better classification performance than other state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%