UL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification

Sun, Weiwei; Halevy, Avner; Benedetto, John J.; Czaja, Wojciech; Liu, Chun; Wu, Hangbin; Shi, Bingjie; Li, Weiyue

doi:10.1016/j.isprsjprs.2013.12.003

Cited by 79 publications

(37 citation statements)

References 40 publications

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“…In this section, we conduct a set of experiments on Indian Pines HSI data se t [6] to further evaluate the effectiveness of the proposed method and the proposed method is also compare with PCA and LDA. The Indian Pines HSI data set is a scene of the Northwest Indiana gathered by the AVIRIS sensor in 1992.It consists of 145×145 pixels and 220 spectral bands within the range of 375-2500 nm.…”

Section: Methodsmentioning

confidence: 99%

A Hyper spectral Images Classification Method Based on Maximum Scatter Discriminant Analysis

2016

ITM Web Conf.

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Abstract.To overcome "small sample size problem" problem faced by some hyper spectral classification methods, the Maximum Scatter Discriminant criterion is used to analyzed hyperspectral data. Maximum Scatter Discriminant analysis searches for the project axes by maximizing the difference of between-class scatter and within-class scatter matrices, which avoid to calculate the inverse of matrices. Experiment results on Indian Pines HSI data set show that the proposed method outperforms the other methods in terms of recognition accuracy. The proposed method is an effective and feasible method for hyper pectral data classification.

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Section: Methodsmentioning

confidence: 99%

A Hyper spectral Images Classification Method Based on Maximum Scatter Discriminant Analysis

2016

ITM Web Conf.

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“…Linear techniques including principal component analysis (PCA) [9], independent component analysis (ICA) [10], and projection pursuit are used more often. Nonlinear dimensionality reduction techniques (nonlinear mapping [11,12], Isomap [13], locally linear embedding [14], laplacian eigenmaps [15]) are used less often due to the high computational complexity of such techniques.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches

Myasnikov¹

2017

Computer Optics

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Unsupervised segmentation of hyperspectral satellite images is a challenging task due to the nature of such images. In this paper, we address this task using the following three-step procedure. First, we reduce the dimensionality of the hyperspectral images. Then, we apply one of classical segmentation algorithms (segmentation via clustering, region growing, or watershed transform). Finally, to overcome the problem of over-segmentation, we use a region merging procedure based on priority queues. To find the parameters of the algorithms and to compare the segmentation approaches, we use known measures of the segmentation quality (global consistency error and rand index) and well-known hyperspectral images.Keywords: hyperspectral image, segmentation, clustering, watershed transform, region growing, region merging, segmentation quality measure, global consistency error, rand index.Citation: Myasnikov EV. Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches. Computer Optics 2017; 41(4): 564-572. DOI: 10.18287/2412DOI: 10.18287/ -6179-2017.Acknowledgments: The reported study was funded by the Russian Foundation for Basic Research (RFBR) grants 16-29-09494 ofi_m and 16-37-00202 mol_a. IntroductionA hyperspectral image is a three-dimensional array having two spatial dimensions, and one spectral dimension. Every pixel of a hyperspectral image is a vector containing hundreds of components corresponding to a wide range of wavelengths. Compared to grayscale and multispectral images, hyperspectral images offer new opportunities allowing to extract information about materials (components) located on images. Thanks to these unique properties, hyperspectral images are used in agriculture, medicine, chemistry and many other fields.However, high dimensionality of hyperspectral images often makes it impossible to directly apply traditional image analysis techniques to such images. For this reason, hyperspectral image analysis became an extensively studying area last years. In this paper, we consider the segmentation of hyperspectral images, which is one of the most important tasks in hyperspectral image analysis [1 -6]. Other important tasks include, for example, classification [7], detection of anomalies [8], etc.Image segmentation is the process of partitioning an image into connected regions with homogenous properties. In image analysis, segmentation methods are usually divided into three classes [1]: feature-based methods, region-based methods, and edge-based methods.Feature-based methods split all image pixels into subsets, based on their values or derived properties. Thus, first class of methods operates in spectral or derived space. This class includes methods based on clustering [2,3]. Region-based and edge-based methods operate on a spatial domain. Region-based methods use some homogeneity criterion to detect regions in an image. This class includes methods based on region growing, and watershed transformation [4,5]. Edge-based methods use the properties of discont...

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“…Detailed spectral information is naturally beneficial for supervised land-cover classification in HSI. However, problems such as Hughes phenomenon can emerge due to the high dimension of HSI data [14]. To alleviate the above-mentioned problem, a lot of methods have been proposed in literature.…”

Section: Introductionmentioning

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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UL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification

Cited by 79 publications

References 40 publications

A Hyper spectral Images Classification Method Based on Maximum Scatter Discriminant Analysis

A Hyper spectral Images Classification Method Based on Maximum Scatter Discriminant Analysis

Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches

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

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