Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction

By Lori Mann Bruce, Cliff H. Koger, Jiang Li. IEEE Transactions on Geoscience and Remote Sensing, 2002

502 citations recorded by the source corpus.

Subfields: Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/944362bbb4081b1fea97a5f18c8c235e5d309209

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Media Technology

Citations in the corpus

Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox (2020, methodology): “Since, DWT does not reduce the dimension, in [30], linear discriminant analysis (LDA) has been exploited to reduce the dimension.”
Feature dimensionality reduction: a review (2022, background): “Wavelet Transform (WT) solves the contradictions between time resolution and frequency resolution well [82].”
Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM (2014, unknown): “logical [16], [17], [21]–[24], Gabor [25], [26], wavelets [27],”
Deep Learning-Based Classification of Hyperspectral Data (2014, methodology): “Transformation is one method available to deal with high dimensionality [15]–[17].”
Feature Mining for Hyperspectral Image Classification (2013, background): “In addition to the above two categories, a wavelet transform FE was proposed to separate the fine-scale and largescale information of a hyperspectral signal [70].”
Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954–2008) (2012, background): “Convolutions with the high-pass filter produced the detail coefficients, which represent the low-scale or high-frequency components (Bruce et al., 2002).”
Deep learning classifiers for hyperspectral imaging: A review (2019, background): “, 2017), (v) dimensionality reduction (Bruce et al., 2002; Haut et al., 2018d) and (vi) data classification (Fauvel et al.”
Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels (2007, unknown): “At level 1, the approximation coefficient (Ai) is an approximation of the low frequencies of A0 and the detail coefficient (Dj) is the high frequency correction for A0 (Bruce et al., 2002), and so on for each subsequent level.”
Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network (2015, unknown): “transformed feature space [18], [19].”
Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations (2005, unknown): “In order to separate fine-scale from large-scale information, multiscale approaches such as derivative spectroscopy [8] and the wavelet transform [9] have been previously applied to extract relevant features from hyperspectral signals.”