Integration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data

Jiménez, Luís Ortiz; Rivera-Medina, Jorge; Rodriguez‐Diaz, Eladio; Arzuaga, Emmanuel; Ramirez-Velez, M.

doi:10.1109/tgrs.2004.843193

Cited by 82 publications

(36 citation statements)

References 5 publications

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“…However, spatial information can play an important role in hyperspectral image classification [24]. Classification accuracy can greatly be improved when spatial and spectral features are effectively combined [25]. In this study, we propose to use a Homogenous Objects Extraction (HOE)-based method to combine spectral and spatial information for classification.…”

Section: Image Classification Methodsmentioning

confidence: 99%

Optimized maximum noise fraction for dimensionality reduction of Chinese HJ-1A hyperspectral data

Gao

Zhang

Sun

et al. 2013

EURASIP J. Adv. Signal Process.

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The important techniques in processing hyperspectral data acquired by interference imaging spectrometer onboard Small Satellite Constellation for Environment and Disaster mitigation (HJ-1A) are studied in this article. First, a new noise estimation method, named residual-scaled local standard deviations, is used to analyze the noise condition of HJ-1A hyperspectral images. Then, an optimized maximum noise fraction (OMNF) transform is proposed for dimensionality reduction of HJ-1A images, which adopts an accurately estimated noise covariance matrix for noise whitening. The proposed OMNF method is less sensitive to noise distribution and interference existence, thus it can more efficiently compact useful data information in a low-dimensional space. The proposed OMNF is evaluated through two applications, i.e., spectral unmixing and classification, using the HJ-1A image acquired at the Bohai Sea area in China. It demonstrates that the proposed OMNF provides better performance in comparison with other traditional dimensionality reduction methods.

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Section: Image Classification Methodsmentioning

confidence: 99%

Optimized maximum noise fraction for dimensionality reduction of Chinese HJ-1A hyperspectral data

Gao

Zhang

Sun

et al. 2013

EURASIP J. Adv. Signal Process.

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“…We compare the proposed framework to the MC-SVM algorithm in the experiment section. Another way to incorporate spatial information is via image segmentation algorithms [17], [18]. The results from these approaches largely depend on the initial segmentation results.…”

Section: A Land-cover Classification With Hyperspectral Datamentioning

confidence: 99%

Spatially Adaptive Classification of Land Cover With Remote Sensing Data

Jun

Ghosh

2011

IEEE Trans. Geosci. Remote Sensing

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Abstract-This paper proposes a novel framework called Gaussian process maximum likelihood for spatially adaptive classification of hyperspectral data. In hyperspectral images, spectral responses of land covers vary over space, and conventional classification algorithms that result in spatially invariant solutions are fundamentally limited. In the proposed framework, each band of a given class is modeled by a Gaussian random process indexed by spatial coordinates. These models are then used to characterize each land cover class at a given location by a multivariate Gaussian distribution with parameters adapted for that location. Experimental results show that the proposed method effectively captures the spatial variations of hyperspectral data, significantly outperforming a variety of other classification algorithms on three different hyperspectral data sets.

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“…Although hyperspectral images allow a better discrimination among similar ground objects than traditional multispectral sensors, the ability for discriminating different vegetation types is limited only based on spectral features. There are many studies that have reported that properly combining multiple features always results in good classification performance [22][23][24][25]. Therefore, this study proposes an integrated scheme for vegetation classification by simultaneously exploiting image spectral and spatial information to improve vegetation classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Improved Combination of Spectral and Spatial Features for Vegetation Classification in Hyperspectral Images

Zhao

Wang

et al. 2017

Remote Sensing

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Due to the advances in hyperspectral sensor technology, hyperspectral images have gained great attention in precision agriculture. In practical applications, vegetation classification is usually required to be conducted first and then the vegetation of interest is discriminated from the others. This study proposes an integrated scheme (SpeSpaVS_ClassPair_ScatterMatrix) for vegetation classification by simultaneously exploiting image spectral and spatial information to improve vegetation classification accuracy. In the scheme, spectral features are selected by the proposed scatter-matrix-based feature selection method (ClassPair_ScatterMatrix). In this method, the scatter-matrix-based class separability measure is calculated for each pair of classes and then averaged as final selection criterion to alleviate the problem of mutual redundancy among the selected features, based on the conventional scatter-matrix-based class separability measure (AllClass_ScatterMatrix). The feature subset search is performed by the sequential floating forward search method. Considering the high spectral similarity among different green vegetation types, Gabor features are extracted from the top two principal components to provide complementary spatial features for spectral features. The spectral features and Gabor features are stacked into a feature vector and then the ClassPair_ScatterMatrix method is used on the formed vector to overcome the over-dimensionality problem and select discriminative features for vegetation classification. The final features are fed into support vector machine classifier for classification. To verify whether the ClassPair_ScatterMatrix method could well avoid selecting mutually redundant features, the mean square correlation coefficients were calculated for the ClassPair_ScatterMatrix method and AllClass_ScatterMatrix method. The experiments were conducted on a widely used agricultural hyperspectral image. The experimental results showed that (1) the The proposed ClassPair_ScatterMatrix method could better alleviate the problem of selecting mutually redundant features, compared to the AllClass_ScatterMatrix method; (2) compared with the representative mutual information-based feature selection methods, the scatter-matrix-based feature selection methods generally achieved higher classification accuracies, and the ClassPair_ScatterMatrix method especially, produced the highest classification accuracies with respect to both data sets (87.2% and 90.1%); and (3) the proposed integrated scheme produced higher classification accuracy, compared with the decision fusion of spectral and spatial features and the methods only involving spectral

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Integration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data

Cited by 82 publications

References 5 publications

Optimized maximum noise fraction for dimensionality reduction of Chinese HJ-1A hyperspectral data

Optimized maximum noise fraction for dimensionality reduction of Chinese HJ-1A hyperspectral data

Spatially Adaptive Classification of Land Cover With Remote Sensing Data

An Improved Combination of Spectral and Spatial Features for Vegetation Classification in Hyperspectral Images

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