Automated detection of subpixel hyperspectral targets with continuous and discrete wavelet transforms

Bruce, Lori; Morgan, Cristine L.S.; Larsen, Samuel

doi:10.1109/36.957284

Cited by 101 publications

(56 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The highest RMSE with q = 10 indicated that the solution closely matching the test dataset might not be the best solution to estimate LAI. We chose the Haar mother wavelet in this study, as it is the simplest of various mother wavelets, and has been found useful for hyperspectral data analysis in previous studies [33,36,39]. Pu and Gong [41] compared different families of wavelets for LAI and crown cover mapping using EO-1 Hyperion data.…”

Section: Discussionmentioning

confidence: 99%

“…We chose the Haar mother wavelet as it is the simplest of all available mother wavelets, and recent investigations have illustrated its effectiveness in hyperspectral data analysis [33,36,39]. The decomposition level is determined by the type of wavelet and the original signal length.…”

Section: Calculation Of Discrete Wavelet Coefficientsmentioning

confidence: 99%

“…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. In this study, we employ the Haar wavelet transform (DWT), which separates a discrete signal f of length n into two subsignals, each with length n/2, one being a running average and the other a running difference [45].…”

Section: Background On Wavelet Transformsmentioning

confidence: 99%

“…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%

See 3 more Smart Citations

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

Section: Discussionmentioning

confidence: 99%

Section: Calculation Of Discrete Wavelet Coefficientsmentioning

confidence: 99%

Section: Background On Wavelet Transformsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

show abstract

“…From previous research in wavelet analysis of hyperspectral signatures, we know that large scale, or global features, can be pertinent to particular target recognition problems. For example, the discrete wavelet transform (DWT) has been successfully used for extracting both local and global spectral features in several hyperspectral target recognition systems [3][4][5][6][7][8]. Bruce et al used the DWT to successfully extract features from hyperspectral signatures [3][4], and interestingly, they found that oftentimes a combination of both small scale and large scale features were optimum.…”

Section: Introductionmentioning

confidence: 99%

Multiclassifiers and decision fusion in the wavelet domain for exploitation of hyperspectral data

West

Prasad

Bruce

2007

2007 IEEE International Geoscience and Remote Sensing Symposium

View full text Add to dashboard Cite

Abstract-In this paper, the discrete wavelet transform (DWT) is employed as a preprocessing stage for a multiclassifier and decision fusion system for feature extraction and dimensionality reduction of hyperspectral data. As a result, both global and local spectral features can be exploited. Feature grouping is conducted according to wavelet decomposition levels, or scales. Each DWT decomposition level's detail coefficients are classified independently, creating a multiclassifer system. The resulting classifications are then fused using a simple majority voting scheme. The proposed target recognition system was applied to hyperspectral data for an agrictultural applications, namely detecting the presence of the often devastating disease known as soybean rust in soybean crops. The proposed approach was compared to well-known hyperspectral dimensionality reduction methods, such as stepwise linear discriminant anlaysis (LDA). When using the DWT multiclassifier system, the overall classification accuracies ranged from the high 80's to the mid 90's. When using the stepwise LDA technique the overall classification accuracies ranged from the mid 60s to the mid 90's. .

show abstract

Automatic Target Recognition in Multispectral and Hyperspectral Imagery Via Joint Transform Correlation

Alam

Sakla

2012

Augmented Vision and Reality

View full text Add to dashboard Cite

Automated detection of subpixel hyperspectral targets with continuous and discrete wavelet transforms

Cited by 101 publications

References 22 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

Multiclassifiers and decision fusion in the wavelet domain for exploitation of hyperspectral data

Automatic Target Recognition in Multispectral and Hyperspectral Imagery Via Joint Transform Correlation

Contact Info

Product

Resources

About