Segmented minimum noise fraction transformation for efficient feature extraction of hyperspectral images

Guan, Lixin; Xie, Weixin; Pei, Jihong

doi:10.1016/j.patcog.2015.04.013

Cited by 49 publications

(19 citation statements)

References 12 publications

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“…2) Data Transformation: Data transformation plays an important role in the proposed ensemble classifier. In this article, minimum noise fraction (MNF) [31] is used as the transformation technique. MNF chooses new components to maximize the signal-to-noise ratio.…”

Section: Ensemble Classifier Designmentioning

confidence: 99%

Inverse Coefficient of Variation Feature and Multilevel Fusion Technique for Hyperspectral and LiDAR Data Classification

Jahan

Zhou

Awrangjeb

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Multisource remote sensing data contain complementary information on land covers, but fusing them is a challenging problem due to the heterogeneous nature of the data. This article aims to extract and integrate information from hyperspectral image (HSI) and light detection and ranging (LiDAR) data for land cover classification. As there is a scarcity of a large number of training samples for remotely sensed hyperspectral and LiDAR data, in this article, we propose a model, which is able to perform impressively using a limited number of training samples by extracting effective features representing different characteristics of objects of interest from these two complementary data sources (HSI and LiDAR). A novel feature extraction method named inverse coefficient of variation (ICV) is introduced for HSI, which considers the Gaussian probability of neighborhood between every pair of bands. We, then, propose a two-stream feature fusion approach to integrate the ICV feature with several features extracted from HSI and LiDAR data. We incorporate a fusion unit named canonical correlation analysis as a basic unit for fusing two different sets of features within each stream. We also incorporate the concept of ensemble classification where the features produced by two-stream fusion are distributed into subsets and transformed to improve the feature quality. We compare our method with the existing state-ofthe-art methods, which are based on deep learning or handcrafted feature extraction or using both of them. Experimental results show that our proposed approach performs better than other existing methods with a limited number of training samples. Index Terms-Canonical correlation analysis (CCA), fusion, hyperspectral, light detection and ranging (LiDAR), multisource.Farah Jahan received the B.Sc.(Hons.) degree in computer science and engineering from the University of Chittagong, Chittagong, Bangladesh, in 2008, the M.E. degree in information and telecommunication engineering

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Section: Ensemble Classifier Designmentioning

confidence: 99%

Inverse Coefficient of Variation Feature and Multilevel Fusion Technique for Hyperspectral and LiDAR Data Classification

Jahan

Zhou

Awrangjeb

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Here we compare the classification performance of several state of art with respect to our proposed model. In this section, a proposed preprocessing MBF-Algorithm will compare with some existing popular feature extraction methods such as MNF [10], PCA [11], SPP [12], LPP [13], MSME [14], and SPA [15]. The kappa coefficient of classification (Kappa) and overall accuracy (OA) has used to show the accuracy after classification.…”

Section: Comparative Studymentioning

confidence: 99%

“…A Hughes effect [7], is related to low generalization ability of the "classifier", which frequently has encountered in several "pattern recognition" applications such as; object recognition, text categorization, computer vision and gene expression data [8], [9]. There are several feature techniques such as "MNF" (minimum-noise fraction [10]), "PCA" (principal component analysis [11]), "SPP" (sparsity preserving projection [12]), "LPP" (local preserving projection [13]), "MSME" (multi-structure manifold embedding [14]), and "SPA" (sparsity preserving analysis [15]), etc. Though the some important information at feature approach has not obtained properly, therefore it causes the performance degradation in HSI classification.…”

Section: Introductionmentioning

confidence: 99%

“…Taking a SalinasS dataset, the classification result of different method has compared with proposed MBF has shown in Table 3. The MBF (PA) is compared with respect to MNF [10], PCA [11], SPP [12], LPP [13], MSME [14], and SPA [15] method, based upon the Kappa (%), OA (%) and their individual class. In Table 3, five classes got accuracy of 100% at MBF algorithm (classes number are 3, 4, 9, 11 and 16).…”

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confidence: 99%

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A Neural Network Approach to Identify Hyperspectral Image Content

Rajegowda¹,

Balamurugan²

2018

IJECE

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<p><span>A Hyperspectral is the imaging technique that contains very large dimension data with the hundreds of channels. Meanwhile, the Hyperspectral Images (HISs) delivers the complete knowledge of imaging; therefore applying a classification algorithm is very important tool for practical uses. The HSIs are always having a large number of correlated and redundant feature, which causes the decrement in the classification accuracy; moreover, the features redundancy come up with some extra burden of computation that without adding any beneficial information to the classification accuracy. In this study, an unsupervised based Band Selection Algorithm (BSA) is considered with the Linear Projection (LP) that depends upon the metric-band similarities. Afterwards Monogenetic Binary Feature (MBF) has consider to perform the ‘texture analysis’ of the HSI, where three operational component represents the monogenetic signal such as; phase, amplitude and orientation. In post processing classification stage, feature-mapping function can provide important information, which help to adopt the Kernel based Neural Network (KNN) to optimize the generalization ability. However, an alternative method of multiclass application can be adopt through KNN, if we consider the multi-output nodes instead of taking single-output node.</span></p>

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“…MNF transforms were used to separate noisy data and to reduce data dimensionality and the workload of subsequent processing. The correlations between any two bands were eliminated after MNF transformation, and noise was reduced [39]. Before MNF was applied, the Landsat OLI 30 m spatial resolution multispectral bands were fused with the 15 m panchromatic band, using the Gram-Schmidt (GS) fusion method.…”

Section: Mixed Pixel Decomposition (1) Establishment Of Spectral Librarymentioning

confidence: 99%

Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods

Yang

Ding

et al. 2017

Remote Sensing

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Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC) information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed pixel influence. The abundance and minimum noise fraction (MNF) results that were obtained from mixed pixel decomposition were added to decision tree multi-features using a three-dimensional (3D) Terrain model, which was created using an image fusion digital elevation model (DEM), to select training samples (ROIs), and improve ROI separability. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the Kappa coefficient and the overall accuracy of integrated pixel unmixing and decision tree method increased by 0.093% and 10%, respectively, as compared with the original decision tree method. This proposed method could effectively eliminate the influence of mixed pixels and improve the accuracy in complex LULC classifications.

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Segmented minimum noise fraction transformation for efficient feature extraction of hyperspectral images

Cited by 49 publications

References 12 publications

Inverse Coefficient of Variation Feature and Multilevel Fusion Technique for Hyperspectral and LiDAR Data Classification

Inverse Coefficient of Variation Feature and Multilevel Fusion Technique for Hyperspectral and LiDAR Data Classification

A Neural Network Approach to Identify Hyperspectral Image Content

Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods

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