Hyperspectral Image Classification Using Nearest Feature Line Embedding Approach

Chang, Yang-Lang; Liu, Jinnan; Han, Chin‐Chuan; Dang, Yingnong

doi:10.1109/tgrs.2013.2238635

Cited by 36 publications

(28 citation statements)

References 23 publications

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“…The proposed methods, NFLE [20,21], KNFLE, FNFLE [26], and FKNFLE, were compared with two state-of-the-art algorithms, i.e., nearest regularized subspace (NRS) [25] and NRS-LFDA [25]. The parameter configurations for both algorithms NRS [29] and NRS-LFDA were as seen in [25].…”

Section: Classification Resultsmentioning

confidence: 99%

“…In this study, three approaches, nearest feature line embedding (NFLE) [20,21], kernelization [15], and fuzzy k nearest neighbor (FKNN) [22], were considered to reduce the feature dimensions for HSI classification. Before the proposed methods, brief reviews of NFLE and kernelization methods are presented in the following:…”

Section: Related Workmentioning

confidence: 99%

“…For more details, refer to [20,21]. Considering the class labels in supervised classification, two parameters 1 K and 2 K are manually determined in calculating the within-class scatter w S and the between-class scatter b S , respectively:…”

Section: Nearest Feature Line Embedding (Nfle)mentioning

confidence: 99%

“…In the previous works, the nearest feature line (NFL) strategy was embedded in the linear transformation for dimension reduction on face recognition [20] and HSI classification [21]. However, the nonlinear and non-Euclidean structures were not efficiently extracted using the linear transformation.…”

Section: Introductionmentioning

confidence: 99%

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A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation

et al. 2015

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Abstract:In this paper, a general nearest feature line (NFL) embedding (NFLE) transformation called fuzzy-kernel NFLE (FKNFLE) is proposed for hyperspectral image (HSI) classification in which kernelization and fuzzification are simultaneously considered. Though NFLE has successfully demonstrated its discriminative capability, the non-linear manifold structure cannot be structured more efficiently by linear scatters using the linear NFLE method. According to the proposed scheme, samples were projected into a kernel space and assigned larger weights based on that of their neighbors. The within-class and between-class scatters were calculated using the fuzzy weights, and the best transformation was obtained by maximizing the Fisher criterion in the kernel space. In that way, the kernelized manifold learning preserved the local manifold structure in a Hilbert space as well as the locality of the manifold structure in the reduced low-dimensional space. The proposed method was compared with various state-of-the-art methods to evaluate the performance using three benchmark data sets. Based on the experimental results: the proposed FKNFLE outperformed the other, more conventional methods.

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Section: Classification Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Nearest Feature Line Embedding (Nfle)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation

et al. 2015

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“…In order to deal with the problems arising as the data dimensionality increases, many Dimensionality Reduction (DR) methods [4,[6][7][8][9][10] have been adopted for HSI classification. These methods fall into three categories: unsupervised, supervised and semi-supervised.…”

Section: Introductionmentioning

confidence: 99%

Spectral-Spatial Response for Hyperspectral Image Classiﬁcation

Wei

Zhou

2017

Remote Sensing

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This paper presents a hierarchical deep framework called Spectral-Spatial Response (SSR) to jointly learn spectral and spatial features of Hyperspectral Images (HSIs) by iteratively abstracting neighboring regions. SSR forms a deep architecture and is able to learn discriminative spectral-spatial features of the input HSI at different scales. It includes several existing spectral-spatial-based methods as special scenarios within a single unified framework. Based on SSR, we further propose the Subspace Learning-based Networks (SLN) as an example of SSR for HSI classification. In SLN, the joint spectral and spatial features are learned using templates simply learned by Marginal Fisher Analysis (MFA) and Principal Component Analysis (PCA). An important contribution to the success of SLN is the exploitation of label information of training samples and the local spatial structure of HSI. Extensive experimental results on four challenging HSI datasets taken from the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) airborne sensors show the implementational simplicity of SLN and verify the superiority of SSR for HSI classification.

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Neighbourhood Feature Space Discriminant Analysis for High Range Resolution Radar Target Recognition

Wang³

et al. 2017

Lecture Notes in Electrical Engineering

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Hyperspectral Image Classification Using Nearest Feature Line Embedding Approach

Cited by 36 publications

References 23 publications

A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation

A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation

Spectral-Spatial Response for Hyperspectral Image Classiﬁcation

Neighbourhood Feature Space Discriminant Analysis for High Range Resolution Radar Target Recognition

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