ICA-Aided Mixed-Pixel Analysis of Hyperspectral Data in Agricultural Land

Kosaka, Naoko; Uto, Kuniaki; Kosugi, Yukio

doi:10.1109/lgrs.2005.846439

Cited by 22 publications

(8 citation statements)

References 8 publications

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“…As a final note, despite the fact that many efforts have been investigated [7]- [9], [33]- [36] to determine the number of endmembers, to the authors' best knowledge, there is no specific technique developed for determining and finding the number of true endmembers. In other words, determining the number of endmembers and extracting endmembers are actually two separate tasks.…”

Section: Uvsfa Versus Endmember Extractionmentioning

confidence: 99%

Component Analysis-Based Unsupervised Linear Spectral Mixture Analysis for Hyperspectral Imagery

Chang

Jiao

et al. 2011

IEEE Trans. Geosci. Remote Sensing

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Two of the most challenging issues in the unsupervised linear spectral mixture analysis (ULSMA) are: 1) determining the number of signatures to form a linear mixing model; and 2) finding the signatures used to unmix data. These two issues do not occur in supervised LSMA since the target signatures are assumed to be known a priori. With recent advances in hyperspectral sensor technology, many unknown and subtle signal sources can now be uncovered and revealed and such signal sources generally cannot be identified by prior knowledge. Even when they can, the obtained knowledge may not be reliable, accurate, or complete. As a consequence, the resulting unmixed results may be misleading. This paper addresses these issues by introducing a new concept of inter-band spectral information (IBSI), which can be used to categorize signatures into background and target classes in terms of their sample spectral statistics. It then develops a component analysis (CA)-based ULSMA where two classes of signatures can be extracted directly from the data by two different CA-based transforms without requiring prior knowledge. In order to substantiate the utility of the proposed approach, synthetic images are used for experiments and real images are further used for validation

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Section: Uvsfa Versus Endmember Extractionmentioning

confidence: 99%

Component Analysis-Based Unsupervised Linear Spectral Mixture Analysis for Hyperspectral Imagery

Chang

Jiao

et al. 2011

IEEE Trans. Geosci. Remote Sensing

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“…Recently, the multistage mode of hyperspectral data analysis has been playing a leading role, where performance of a classification system is supported by a set of analyses, such as segmentation, feature extraction and optimization, and dimensionality reduction. The empirical mode decomposition (EMD) for SVM classifier (Demir and Ertürk 2010), nonparametric feature extraction aided K‐NN classifier (Yang et al ), Bayesian learning‐based probabilistic sparse kernel aided (RVM, Mianji and Zhang ), posterior probability SVM (PPSVM, Wang et al ), kernel local Fisher discriminant analysis (KLFDA, Li W et al ), SVM with extended morphological attribute profiles and ICA (Kosaka et al ), ICDA framework (Villa et al ), adaptive MRF approach with SVM (Li S et al ), Canonical Bayesian classifiers (Zhang L et al ), mixture analysis and DT classifier (Delalieux et al ), and the feature selective linguistic classifier (Samadzadegan et al ) are becoming dominant approaches for hyperspectral data analysis. Imani et al () has proposed band clustering‐based feature extraction for the classification of hyperspctral data using limited training samples.…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral Remote Sensing Classifications: A Perspective Survey

Chutia

Bhattacharyya

Sarma

et al. 2015

Transactions in GIS

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Classification of hyperspectral remote sensing data is more challenging than multispectral remote sensing data because of the enormous amount of information available in the many spectral bands. During the last few decades, significant efforts have been made to investigate the effectiveness of the traditional multispectral classification approaches on hyperspectral data. Formerly extensively established conventional classification methods have been dominated by the advanced classification approaches and many pre-processing techniques have been developed and incorporated in hyperspectral classification. A perspective survey of hyperspectral remote sensing classification approaches is presented here. It comprehensively highlights the taxonomy of major classification approaches reported during the last two decades and describes an experimental evaluation of a few major classification algorithms. Recent advancements in the development of classification approaches are also emphasized with a set of guidelines for achieving better classification performances.

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“…Apply the ICA technique by including the green peak as an input to HS data. Independent components are identified as a component and noise by normalized and zero mean respectively [17].…”

Section: B Independent Component Analysismentioning

confidence: 99%

The Novel Gravitational Mass Weighted PCA Technique for Feature Extraction in Hyperspectral Data Classification

Nandhini¹,

Porkodi²

2019

IJEAT

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Recent advancements in the imaging spectrometer collect both spatial and spectral information which creates a huge dimensionality. The heavy spectral information creates to build a classifier for discerning between the materials in the scene. The minimum number of training labels always in an exchange between the spectral information and the performance is called the Hughes effect. Also the redundant of spectral information and noisy data presents in the hyperspectral scene. The above issues are overcome using feature extraction and feature selection methods which play a major role in the reduction of dimensionality. This paper proposes the novel fusion Gravitational Mass Weighted Principal Component Analysis (GMWPCA) techniques for hyperspectral data dimensionality. Also, this paper presents the deep insight about the feature extraction techniques in hyperspectral data of both supervised and unsupervised learning methods and experimental analysis in AVIRIS Indian Pines hyperspectral dataset by employing PCA, Probability PCA, LDA, and proposed techniques. The 93.63 % high accuracy achieved by using a novel proposed method.

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ICA-Aided Mixed-Pixel Analysis of Hyperspectral Data in Agricultural Land

Cited by 22 publications

References 8 publications

Component Analysis-Based Unsupervised Linear Spectral Mixture Analysis for Hyperspectral Imagery

Component Analysis-Based Unsupervised Linear Spectral Mixture Analysis for Hyperspectral Imagery

Hyperspectral Remote Sensing Classifications: A Perspective Survey

The Novel Gravitational Mass Weighted PCA Technique for Feature Extraction in Hyperspectral Data Classification

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