Automatic reduction of hyperspectral imagery using wavelet spectral analysis

Kaewpijit, Sinthop; Moigne, Jacqueline Le; El‐Ghazawi, Tarek

doi:10.1109/tgrs.2003.810712

Cited by 178 publications

(97 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several studies have employed wavelet transform (WT) to overcome this problem [22,31], because WT can preserve the peaks and valleys among spectral signatures and magnify subtle spectral features [22,32]. WT breaks up a signal into a set of shifted and scaled versions of the original (or mother) wavelet, resulting in approximation (low-pass filter) coefficients and detail (high-pass filter) coefficients at various decomposition levels [33,34].…”

Section: Introductionmentioning

confidence: 99%

A Wavelet-Based Area Parameter for Indirectly Estimating Copper Concentration in Carex Leaves from Canopy Reflectance

Wang

Shi

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

Due to the absence of evident absorption features and low concentrations, the copper (Cu) concentration in plant leaves has rarely been estimated from hyperspectral remote sensing data. The capability of remotely-sensed estimation of foliar Cu concentrations largely depends on its close relation to foliar chlorophyll concentration. To enhance the subtle spectral changes related to chlorophyll concentration under Cu stress, this study described a wavelet-based area parameter (SWT (605−720), the sum of reconstructed detail reflectance at fourth decomposition level over 605−720 nm using discrete wavelet transform) from the canopy hyperspectral reflectance (350−2500 nm, N = 71) of Carex (C. cinerascens). The results showed that Cu concentrations had negative and strong correlation with chlorophyll concentrations (r = -0.719, p < 0.001). Based on 1000 random dataset partitioning experiments, the 1000 linear calibration models provided a mean R 2 Val (determination coefficient of validation) value of 0.706 and an RPD (residual prediction deviation) value of 1.75 for Cu estimation. The bootstrapping and ANOVA test results showed that SWT (605−720) significantly (p < 0.05) outperformed published chlorophyll-related and wavelet-based spectral parameters. It was concluded here that the wavelet-based area parameter (i.e., SWT (605−720)) has potential ability to indirectly estimate OPEN ACCESSRemote Sens. 2015, 7 15341Cu concentrations in Carex leaves through the strong correlation between Cu and chlorophyll. The method presented in this pilot study may be used to estimate the concentrations of other heavy metals. However, further research is needed to test its transferability and robustness for estimating Cu concentrations on other plant species in different biological and environmental conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

A Wavelet-Based Area Parameter for Indirectly Estimating Copper Concentration in Carex Leaves from Canopy Reflectance

Wang

Shi

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The discrete wavelet transform (DWT) has been increasingly used in recent years in the processing of hyperspectral images for a variety of purposes [33][34][35][36][37][38][39]. These studies have shown its importance as an effective tool for reducing the dimensionality of hyperspectral data while maintaining information content for a variety of applications.…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…But the usefulness of these methods in the context of a general rendering system, with no assumptions on sensors, surfaces, and lights requires more investigations [73]. Recently, Kaewpijit et al [26] applied wavelet decomposition in the context of hyperspectral imagery [57] for the identification of ground surface pixels. They showed that wavelets preserve the distinctions between spectral signatures and yield better classification accuracy than PCA at the same level of compression rate.…”

Section: Separable Decompositionsmentioning

confidence: 99%

A wavelet-based framework for acquired radiometric quantity representation and accurate physical rendering

2006

View full text Add to dashboard Cite

In this paper, we present a framework based on a generic representation, which is able to handle most of the radiometric quantities required by global illumination softwares. A sparse representation in the wavelet space is built using the separation between the directional and the wavelength dependence of such radiometric quantities. Particularly, we show how to use this representation for spectral power distribution, spectral reflectance and phase function measurements modelling. Then, we explain how the representation is useful to perform spectral rendering. On one hand, it speeds up spectral path tracing by importance sampling to generate reflected directions and by avoiding expensive computations usually done on the fly. On the other hand, it allows efficient spectral photon mapping, both in terms of memory and speed. We also show how complex light emission from real luminaires can be efficiently sampled to emit photons with our numerical model.

show abstract

Automatic reduction of hyperspectral imagery using wavelet spectral analysis

Cited by 178 publications

References 13 publications

A Wavelet-Based Area Parameter for Indirectly Estimating Copper Concentration in Carex Leaves from Canopy Reflectance

A Wavelet-Based Area Parameter for Indirectly Estimating Copper Concentration in Carex Leaves from Canopy Reflectance

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

A wavelet-based framework for acquired radiometric quantity representation and accurate physical rendering

Contact Info

Product

Resources

About