Feature extraction of hyperspectral images using wavelet and matching pursuit

Hsu, Pai-Hui

doi:10.1016/j.isprsjprs.2006.12.004

Cited by 118 publications

(85 citation statements)

References 26 publications

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“…A wavelet transform enables signal (data) analysis at different scales or resolutions by creating a series of shifted and scaled versions of the mother wavelet function [33,44]. The term "mother" implies that a set of basis functions {ψ α,b (λ)} can be generated from one main function, or the mother wavelet ψ(λ) by the following equation [36]: (1) where a is the scaling factor of a particular basis function, b is the translation variable along the function's range and λ is a subset of an analyzed signal.…”

Section: Background On Wavelet Transformsmentioning

confidence: 99%

Investigating the Utility of Wavelet Transforms for Inverting a 3-D Radiative Transfer Model Using Hyperspectral Data to Retrieve Forest LAI

et al. 2013

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Abstract:The need for an efficient and standard technique for optimal spectral sampling of hyperspectral data during the inversion of canopy reflectance models has been the subject of many studies. The objective of this study was to investigate the utility of the discrete wavelet transform (DWT) for extracting useful features from hyperspectral data with which forest LAI can be estimated through inversion of a three dimensional radiative transfer model, the Discrete Anisotropy Radiative Transfer (DART) model. DART, coupled with the leaf optical properties model PROSPECT, was inverted with AVIRIS data using a look-up-table (LUT)-based inversion approach. We used AVIRIS data and in situ LAI measurements from two different hardwood forested sites in Wisconsin, USA. Prior to inversion, model-simulated and AVIRIS hyperspectral data were transformed into discrete wavelet coefficients using Haar wavelets. The LUT inversion was performed with three different datasets, the original reflectance bands, the full set of wavelet extracted features, and two wavelet subsets containing 99.99% and 99.0% of the cumulative energy The results indicate that the discrete wavelet transform can increase the accuracy of LAI estimates by improving the LUT-based inversion of DART (and, potentially, by implication, other terrestrial radiative transfer models) using hyperspectral data. The improvement in accuracy of LAI estimates is potentially due to different properties of wavelet analysis such as multi-scale representation, dimensionality reduction, and noise removal.

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Section: Background On Wavelet Transformsmentioning

confidence: 99%

Investigating the Utility of Wavelet Transforms for Inverting a 3-D Radiative Transfer Model Using Hyperspectral Data to Retrieve Forest LAI

et al. 2013

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“…Therefore, it is feasible to detect the singularity of reflectance spectra by using the wavelet transform. Hsu et al have proposed the discrete wavelet transform (DWT) and wavelet packet transform to extract lower dimensional spectral features for classification of hyperspectral images [6]. The DWT is proposed for feature extraction and signature classification; however, the authors mention that DWT is not shift invariant [7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Lifting Transform for Classification of Hyperspectral Signatures

Agrawal¹,

Bawane²

2015

ARS

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Supervised classification of hyperspectral images is a challenging task because of the higher dimensionality of a pixel signature. The conventional classifiers require large training data set; however, practically limited numbers of labeled pixels are available due to complexity and cost involved in sample collection. It is essential to have a method that can reduce such higher dimensional data into lower dimensional feature space without the loss of useful information. For classification purpose, it will be useful if such a method takes into account the nature of the underlying signal when extracting lower dimensional feature vector. The lifting framework provides the required flexibility. This article proposes the adaptive lifting wavelet transform to extract the lower dimensional feature vectors for the classification of hyperspectral signatures. The proposed adaptive update step allows the decomposition filter to adapt to the input signal so as to retain the desired characteristics of the signal. A three-layer feed forward neural network is used as a supervised classifier to classify the extracted features. The effectiveness of the proposed method is tested on two hyperspectral data sets (HYDICE & ROSIS sensors). The performance of the proposed method is compared with first generation discrete wavelet transform (DWT) based feature extraction method and previous studies that use the same data. The indices used for measuring performance are overall classification accuracy and Kappa value. The experimental results show that the proposed adaptive lifting scheme (ALS) has excellent results with a small size training set.

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“…For instance, it is ineffective when the traditional statistical classification methods are applied to hyperspectral images with limited training samples. In other words, the dimensionality increases with the number of bands, the number of training samples for classification should be increased as well (Hsu, 2007). This has been termed the "curse of dimensionality" by Bellman (1961).…”

Section: Introductionmentioning

confidence: 99%

“…The commonly used method to solve "curse of dimensionality" is dimensionality reduction, which can be divided into two types: feature selection and feature extraction. For hyperspectral images, feature extraction is used to reduce the dimensionality more frequently (Hsu, 2003).…”

Section: Introductionmentioning

confidence: 99%

Comparisom of Wavelet-Based and HHT-Based Feature Extraction Methods for Hyperspectral Image Classification

Huang

Hsu

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Hyperspectral images, which contain rich and fine spectral information, can be used to identify surface objects and improve land use/cover classification accuracy. Due to the property of high dimensionality of hyperspectral data, traditional statistics-based classifiers cannot be directly used on such images with limited training samples. This problem is referred as "curse of dimensionality." The commonly used method to solve this problem is dimensionality reduction, and feature extraction is used to reduce the dimensionality of hyperspectral images more frequently. There are two types of feature extraction methods. The first type is based on statistical property of data. The other type is based on time-frequency analysis. In this study, the time-frequency analysis methods are used to extract the features for hyperspectral image classification. Firstly, it has been proven that wavelet-based feature extraction provide an effective tool for spectral feature extraction. On the other hand, Hilbert-Huang transform (HHT), a relative new time-frequency analysis tool, has been widely used in nonlinear and nonstationary data analysis. In this study, wavelet transform and HHT are implemented on the hyperspectral data for physical spectral analysis. Therefore, we can get a small number of salient features, reduce the dimensionality of hyperspectral images and keep the accuracy of classification results. An AVIRIS data set is used to test the performance of the proposed HHT-based feature extraction methods; then, the results are compared with waveletbased feature extraction. According to the experiment results, HHT-based feature extraction methods are effective tools and the results are similar with wavelet-based feature extraction methods.

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Feature extraction of hyperspectral images using wavelet and matching pursuit

Cited by 118 publications

References 26 publications

Investigating the Utility of Wavelet Transforms for Inverting a 3-D Radiative Transfer Model Using Hyperspectral Data to Retrieve Forest LAI

Investigating the Utility of Wavelet Transforms for Inverting a 3-D Radiative Transfer Model Using Hyperspectral Data to Retrieve Forest LAI

Adaptive Lifting Transform for Classification of Hyperspectral Signatures

Comparisom of Wavelet-Based and HHT-Based Feature Extraction Methods for Hyperspectral Image Classification

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