An Efficient Method for Supervised Hyperspectral Band Selection

Yang, He; Du, Qian; Su, Hongjun; Sheng, Yehua

doi:10.1109/lgrs.2010.2053516

Cited by 251 publications

(108 citation statements)

References 13 publications

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“…As noted in the introduction, one major issue arising from this approach is how to deal with redundant bands caused by band correlation. As an alternative, another BP-derived SQMBS method is to specify a particular application such as minimum estimated abundance covariance (MEAC) for classification [34], which can generate feature vectors for BP and then takes advantage of the sequential forward floating search (SFFS) and sequential backward floating search (SBFS) developed in [73] to derive forward and backward BS methods. However, the band correlation issue still remains.…”

Section: Band Subset Selectionmentioning

confidence: 99%

Band Subset Selection for Hyperspectral Image Classification

Song

Chang

2018

Remote Sensing

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This paper develops a new approach to band subset selection (BSS) for hyperspectral image classification (HSIC) which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS), rather than one band at a time sequentially, referred to as sequential multiple band selection (SQMBS), as most traditional band selection methods do. In doing so, a criterion is particularly developed for BSS that can be used for HSIC. It is a linearly constrained minimum variance (LCMV) derived from adaptive beamforming in array signal processing which can be used to model misclassification errors as the minimum variance. To avoid an exhaustive search for all possible band subsets, two numerical algorithms, referred to as sequential (SQ) and successive (SC) algorithms are also developed for LCMV-based SMMBS, called SQ LCMV-BSS and SC LCMV-BSS. Experimental results demonstrate that LCMV-based BSS has advantages over SQMBS.

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Section: Band Subset Selectionmentioning

confidence: 99%

Band Subset Selection for Hyperspectral Image Classification

Song

Chang

2018

Remote Sensing

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“…In an unsupervised case, the most informative and distinctive bands are selected according to certain searching criteria. In this paper we adopt an unsupervised method in [10] and a supervised method in [11] due to their excellent performance and simple implementation. Both algorithms were designed using spectral unmixing related concepts in conjunction with sequential forward search strategy.…”

Section: Band Selection Based On the Difference Imagementioning

confidence: 99%

“…The algorithm can continue to select more bands. A minimum estimated abundance covariance (MEAC) method was proposed for supervised band selection [11]. Assume that a given pixel z can be expressed according to a linear mixture model:…”

Section: Main Descriptionsmentioning

confidence: 99%

Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images

Tong

Samat

et al. 2017

Remote Sensing

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This paper proposes to use band selection-based dimensionality reduction (BS-DR) technique in addressing a challenging multi-temporal hyperspectral images change detection (HSI-CD) problem. The aim of this work is to analyze and evaluate in detail the CD performance by selecting the most informative band subset from the original high-dimensional data space. In particular, for cases where ground reference data are available or unavailable, either supervised or unsupervised CD approaches are designed. The following sub-problems in HSI-CD are investigated, including: (1) the estimated number of multi-class changes; (2) the binary CD; (3) the multiple CD; (4) the estimated optimal number of selected bands; and (5) computational efficiency. The main contribution of this paper is to provide for the first time a thorough analysis of the impacts of band selection on the HSI-CD problem, thus to fix the gap in the state-of-the-art techniques either by simply utilizing the full dimensionality of the data or exploring a complex hierarchical change analysis. It is applicable to CD problems in multispectral or PolSAR images when the feature space is expanded for discriminant feature extraction. Two real multi-temporal hyperspectral Hyperion datasets are used to validate the proposed approaches. Quantitative and qualitative experimental results demonstrated that by selecting a subset of the most informative and distinct spectral bands, the proposed approaches offered better CD performance than the state-of-the-art techniques using original full bands, without losing the change representative and discriminable capabilities of a detector.

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“…This procedure usually results in a loss of accuracy as the data dimensionality increases, which is called the curse of dimensionality (Chen andZhang, 2011, Kaya et al, 2011). Therefore, the most important and urgent issue is how to reduce the number of those bands largely, but without loss of information (Yang et al, 2011, Paskaleva et al, 2008.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Marginal Fisher Analysis for Hyperspectral Image Classification

Hong

Liu

Pan

2012

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

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ABSTRACT:The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification. In this paper, we proposed a novel method, called semi-supervised marginal Fisher analysis (SSMFA), to process HSI of natural scenes, which uses a combination of semi-supervised learning and manifold learning. In SSMFA, a new difference-based optimization objective function with unlabeled samples has been designed. SSMFA preserves the manifold structure of labeled and unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution, and it can be computed based on eigen decomposition. Classification experiments with a challenging HSI task demonstrate that this method outperforms current state-of-the-art HSI-classification methods.

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An Efficient Method for Supervised Hyperspectral Band Selection

Cited by 251 publications

References 13 publications

Band Subset Selection for Hyperspectral Image Classification

Band Subset Selection for Hyperspectral Image Classification

Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images

Semi-Supervised Marginal Fisher Analysis for Hyperspectral Image Classification

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