A Band Selection Technique for Spectral Classification

Backer, S. De; Kempeneers, Pieter; Debruyn, Walter; Scheunders, Paul

doi:10.1109/lgrs.2005.848511

Cited by 111 publications

(50 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These differences partly account for the higher error rates seen in the classification of the spectra from the image. Other forthcoming band selection techniques such as those presented by De Backer et al (2005) and Huang and He (2005) or the selection of the most significant wavelet approximation and detail coefficients (rather than the energy feature vector) may also improve classification accuracy at the crown level. The effects of scale are an important determinant of the information that can be derived from the spectra.…”

Section: Discussionmentioning

confidence: 99%

“…Such analyses could subsequently be used for the quantification of the changes of liana extent over time. A variety of data analysis techniques such as wavelet decomposition (Chan & Peng, 2003;Misiti et al, 1996), feature selection (De Backer et al, 2005;Duin, 2000) and pattern recognition (Bishop, 1995;Fukunaga, 1990;Ripley, 1996) have been developed that can take advantage of the large amount of data from hyperspectral sensors. Here we address which common and readily available techniques are most appropriate for the discrimination of the spectra of a community of lianas from canopy trees at the leaf and crown levels.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels

Kalácska

Bohlman

Sánchez‐Azofeifa

et al. 2007

Remote Sensing of Environment

109

View full text Add to dashboard Cite

A dataset of spectral signatures (leaf level) of tropical dry forest trees and lianas and an airborne hyperspectral image (crown level) are used to test three hyperspectral data reduction techniques (principal component analysis, forward feature selection and wavelet energy feature vectors) along with pattern recognition classifiers to discriminate between the spectral signatures of lianas and trees. It was found at the leaf level the forward waveband selection method had the best results followed by the wavelet energy feature vector and a form of principal component analysis. For the same dataset our results indicate that none of the pattern recognition classifiers performed the best across all reduction techniques, and also that none of the parametric classifiers had the overall lowest training and testing errors. At the crown level, in addition to higher testing error rates (7%), it was found that there was no optimal data reduction technique. The significant wavebands were also found to be different between the leaf and crown levels. At the leaf level, the visible region of the spectrum was the most important for discriminating between lianas and trees whereas at the crown level the shortwave infrared was also important in addition to the visible and near infrared.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels

Kalácska

Bohlman

Sánchez‐Azofeifa

et al. 2007

Remote Sensing of Environment

109

View full text Add to dashboard Cite

show abstract

“…Variants such as Sequential Forward Floating Search (SFFS) or Sequential Backward Search (SBFS) are proposed in (Pudil et al, 1994). Among stochastic optimisation strategies used for feature selection, several algorithms have been used for feature selection, including Genetic algorithms (Li et al, 2011, Estévez et al, 2009), Particle Swarm Optimisation (PSO) (Yang et al, 2012) or simulated annealing (De Backer et al, 2005, Chang et al, 2011.…”

Section: Feature Selection: State-of-the-artmentioning

confidence: 99%

Spectral Band Selection for Urban Material Classification Using Hyperspectral Libraries

Bris¹,

Chehata²,

Briottet³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:In urban areas, information concerning very high resolution land cover and especially material maps are necessary for several city modelling or monitoring applications. That is to say, knowledge concerning the roofing materials or the different kinds of ground areas is required. Airborne remote sensing techniques appear to be convenient for providing such information at a large scale. However, results obtained using most traditional processing methods based on usual red-green-blue-near infrared multispectral images remain limited for such applications. A possible way to improve classification results is to enhance the imagery spectral resolution using superspectral or hyperspectral sensors. In this study, it is intended to design a superspectral sensor dedicated to urban materials classification and this work particularly focused on the selection of the optimal spectral band subsets for such sensor. First, reflectance spectral signatures of urban materials were collected from 7 spectral libraires. Then, spectral optimization was performed using this data set. The band selection workflow included two steps, optimising first the number of spectral bands using an incremental method and then examining several possible optimised band subsets using a stochastic algorithm. The same wrapper relevance criterion relying on a confidence measure of Random Forests classifier was used at both steps. To cope with the limited number of available spectra for several classes, additional synthetic spectra were generated from the collection of reference spectra: intra-class variability was simulated by multiplying reference spectra by a random coefficient. At the end, selected band subsets were evaluated considering the classification quality reached using a rbf svm classifier. It was confirmed that a limited band subset was sufficient to classify common urban materials. The important contribution of bands from the Short Wave Infra-Red (SWIR) spectral domain (1000 -2400 nm) to material classification was also shown.

show abstract

“…(Serpico and Bruzzone, 2001) proposes variants of these methods called Steepest Ascent (SA) algorithms. Among stochastic optimization strategies used for feature selection, several algorithms have been used for feature selection, including Genetic algorithms (Li et al, 2011, Estévez et al, 2009, Minet et al, 2010, Particle Swarm Optimization (PSO) (Yang et al, 2012) or simulated annealing (De Backer et al, 2005, Chang et al, 2011). …”

Section: Feature Selectionmentioning

confidence: 99%

Extraction of Optimal Spectral Bands Using Hierarchical Band Merging Out of Hyperspectral Data

Bris¹,

Chehata

Briottet

et al. 2015

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

View full text Add to dashboard Cite

ABSTRACT:Spectral optimization consists in identifying the most relevant band subset for a specific application. It is a way to reduce hyperspectral data huge dimensionality and can be applied to design specific superspectral sensors dedicated to specific land cover applications. Spectral optimization includes both band selection and band extraction. On the one hand, band selection aims at selecting an optimal band subset (according to a relevance criterion) among the bands of a hyperspectral data set, using automatic feature selection algorithms. On the other hand, band extraction defines the most relevant spectral bands optimizing both their position along the spectrum and their width. The approach presented in this paper first builds a hierarchy of groups of adjacent bands, according to a relevance criterion to decide which adjacent bands must be merged. Then, band selection is performed at the different levels of this hierarchy. Two approaches were proposed to achieve this task : a greedy one and a new adaptation of an incremental feature selection algorithm to this hierarchy of merged bands.

show abstract

A Band Selection Technique for Spectral Classification

Cited by 111 publications

References 13 publications

Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels

Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels

Spectral Band Selection for Urban Material Classification Using Hyperspectral Libraries

Extraction of Optimal Spectral Bands Using Hierarchical Band Merging Out of Hyperspectral Data

Contact Info

Product

Resources

About