Locality-Preserving Discriminant Analysis in Kernel-Induced Feature Spaces for Hyperspectral Image Classification

Li, Wei; Prasad, Saurabh; Fowler, James E.; Bruce, Lori

doi:10.1109/lgrs.2011.2128854

Cited by 111 publications

(49 citation statements)

References 20 publications

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“…However, the high-dimensional nature of HSI data creates complications for k-NN classification in terms of both computational complexity and classification accuracy. Many dimensionality-reducing techniques have been proposed to combat this so-called curse of dimensionality, such as the popular linear discriminant analysis (LDA) [8] and its variants (e.g., [9], [10]). Typically, parametric classification is employed after dimensionality reduction, for example the maximum likelihood estimation (MLE) [11] of posterior probabilities.…”

Section: Introductionmentioning

confidence: 99%

Nearest Regularized Subspace for Hyperspectral Classification

Tramel

Prasad

et al. 2014

IEEE Trans. Geosci. Remote Sensing

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218

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Abstract-A classifier that couples nearest-subspace classification with a distance-weighted Tikhonov regularization is proposed for hyperspectral imagery. The resulting nearest-regularizedsubspace classifier seeks an approximation of each testing sample via a linear combination of training samples within each class. The class label is then derived according to the class which best approximates the test sample. The distance-weighted Tikhonov regularization is then modified by measuring distance within a locality-preserving lower-dimensional subspace. Furthermore, a competitive process among the classes is proposed to simplify parameter tuning. Classification results for several hyperspectral image data sets demonstrate superior performance of the proposed approach when compared to other, more traditional classification techniques.

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Section: Introductionmentioning

confidence: 99%

Nearest Regularized Subspace for Hyperspectral Classification

Tramel

Prasad

et al. 2014

IEEE Trans. Geosci. Remote Sensing

Self Cite

218

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show abstract

“…Firstly, the 3D-Gabor filters with various frequencies and directions are adopted for converting original HSI into multiple data cubes, which can provide different spectral-spatial information between each other. Sequentially, a cube evaluation criterion, which is motivated by the Fisher's ratio criteria (FR) [44] and conditional mutual information [45], is proposed to calculate the sufficiency and independency of the cubes obtained from the first step. Cubes which have qualified assessment are regarded as views in our proposed method.…”

Section: Proposed Methodsmentioning

confidence: 99%

3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification

Hu¹,

He²,

Li³

et al. 2018

Remote Sensing

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Active learning (AL) has been shown to be very effective in hyperspectral image (HSI) classification. It significantly improves the performance by selecting a small quantity of the most informative training samples to reduce the complexity of classification. Multiview AL (MVAL) can make the comprehensive analysis of both object characterization and sampling selection in AL by using various features of multiple views. However, the original MVAL cannot effectively exploit the spectral-spatial information by respecting the three-dimensional (3D) nature of the HSI and the query selection strategy in the MVAL is only based on the disagreement of multiple views. In this paper, we propose a 3D-Gabor inspired MVAL method for spectral-spatial HSI classification, which consists of two main steps. First, in the view generation step, we adopt a 3D-Gabor filter to generate multiple cubes with limited bands and utilize the feature assessment strategies to select cubes for constructing views. Second, in the sampling selection step, a novel method is proposed by using both internal and external uncertainty estimation (IEUE) of views. Specifically, we use the distributions of posterior probability to learn the "internal uncertainty" of each independent view, and adopt the inconsistencies between views to estimate the "external uncertainty". Classification accuracies of the proposed method for the four benchmark HSI datasets can be as high as 99.57%, 99.93%, 99.02%, 98.82%, respectively, demonstrating the improved performance as compared with other state-of-the-art methods.

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“…where x i and x j are vectors of observations, σ = sσ 0 , σ 0 is the mean distance between the observations in feature space and s is a scale factor [33,37]. Figures 7c and 12c show the sensitivity of KPCA, KMNF, and OKMNF with respect to s. We can see that both OKMNF and KPCA show better performance than KMNF.…”

Section: Parameter Tuningmentioning

confidence: 95%

Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification

et al. 2017

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This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear dimensionality reduction method. KMNF can map the original data into a higher dimensional feature space and provide a small number of quality features for classification and some other post processing. Noise estimation is an important component in KMNF. It is often estimated based on a strong relationship between adjacent pixels. However, hyperspectral images have limited spatial resolution and usually have a large number of mixed pixels, which make the spatial information less reliable for noise estimation. It is the main reason that KMNF generally shows unstable performance in feature extraction for classification. To overcome this problem, this paper exploits the use of a more accurate noise estimation method to improve KMNF. We propose two new noise estimation methods accurately. Moreover, we also propose a framework to improve noise estimation, where both spectral and spatial de-correlation are exploited. Experimental results, conducted using a variety of hyperspectral images, indicate that the proposed OKMNF is superior to some other related dimensionality reduction methods in most cases. Compared to the conventional KMNF, the proposed OKMNF benefits significant improvements in overall classification accuracy.

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Locality-Preserving Discriminant Analysis in Kernel-Induced Feature Spaces for Hyperspectral Image Classification

Cited by 111 publications

References 20 publications

Nearest Regularized Subspace for Hyperspectral Classification

Nearest Regularized Subspace for Hyperspectral Classification

3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification

Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification

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