Segmented Principal Component Analysis for Parallel Compression of Hyperspectral Imagery

Du, Qian; Zhu, Wei; Yang, He; Fowler, James E.

doi:10.1109/lgrs.2009.2024175

Cited by 45 publications

(1 citation statement)

References 7 publications

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“…The problems around high dimensionality in object recognition from HSIs is currently addressed through classic dimensionality reduction approaches such as feature extraction and feature selection [7,8] methods. Feature extraction approaches, such as Principal Component Analysis (PCA) [9], Minimum Noise Fraction (MNF) [10], Segmented PCA [11], Segmented MNF [12], and Non-Negative Matrix Factorization (NMF) [13], aim to identify a compact set of features that represent the most important information in the data. Feature selection approaches, such as correlation-based and mutual information-based methods, focus on identifying the most relevant features and eliminating the less important ones.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Hyperspectral Object Classification Approach via Imbalanced Training Samples Handling

Islam

Uddin

et al. 2023

Remote Sensing

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Object classification in hyperspectral images involves accurately categorizing objects based on their spectral characteristics. However, the high dimensionality of hyperspectral data and class imbalance pose significant challenges to object classification performance. To address these challenges, we propose a framework that incorporates dimensionality reduction and re-sampling as preprocessing steps for a deep learning model. Our framework employs a novel subgroup-based dimensionality reduction technique to extract and select the most informative features with minimal redundancy. Additionally, the data are resampled to achieve class balance across all categories. The reduced and balanced data are then processed through a hybrid CNN model, which combines a 3D learning block and a 2D learning block to extract spectral–spatial features and achieve satisfactory classification accuracy. By adopting this hybrid approach, we simplify the model while improving performance in the presence of noise and limited sample size. We evaluated our proposed model on the Salinas scene, Pavia University, and Kennedy Space Center benchmark hyperspectral datasets, comparing it to state-of-the-art methods. Our object classification technique achieves highly promising results, with overall accuracies of 99.98%, 99.94%, and 99.46% on the three datasets, respectively. This proposed approach offers a compelling solution to overcome the challenges of high dimensionality and class imbalance in hyperspectral object classification.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Hyperspectral Object Classification Approach via Imbalanced Training Samples Handling

Islam

Uddin

et al. 2023

Remote Sensing

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Spectral compression using subspace clustering

Agahian

Funt

2015

Color Research & Application

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This article describes a subspace clustering strategy for the spectral compression of multispectral images. Unlike standard principal component analysis, this approach finds clusters in several different subspaces of different dimension. Consequently, instead of representing all spectra in a single low‐dimensional subspace of a fixed dimension, spectral data are assigned to multiple subspaces having a range of dimensions from one to eight. In other words, this strategy allows us to distribute spectra into different subspaces thereby obtaining the best fit for each. As a result, more resources can be allocated to those spectra that need many dimensions for accurate representation and fewer resources to those that can be modeled using fewer dimensions. For a given compression ratio, this trade off reduces the overall reconstruction error. In the case of compressing multispectral images, this initial compression method is followed by JPEG2000 compression in order to remove the spatial redundancy in the data as well. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 7–15, 2016

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