Noise reduction and best band selection techniques for improving classification results using hyperspectral data: application to lithological mapping in Canada's Arctic

Harris, Jeff; Пономарев, П. В.; Shang, Jiali; Rogge, Derek

doi:10.5589/m06-029

Cited by 14 publications

(4 citation statements)

References 8 publications

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“…The minimum noise fraction (MNF) (Harris et al ), smoothed estimated reliability measure (SERM, Bachmann et al ), class‐based version of PCA (CPCA, Miao et al ) and MRF are a few well‐known techniques to enhance classification performance. Morphological profiles (MPs) (Licciardi et al ) have been proposed in the recent literature to achieve better results for the classification of remotely sensed data.…”

Section: Associated Pre‐processing Techniques and Instrumentsmentioning

confidence: 99%

“…Comparatively, PCA has been found much simpler, computationally more robust, and more efficient, as it does not require prior statistical information (Chutia et al ). On the other hand, MNF is the second major algorithm belonging to the family of PCA techniques; however, it requires prior estimation of signal and noise covariance matrices and it is computationally more expensive than PCA (Harris et al ). ICA is also computationally more expensive, which limits its application to high‐dimensional data analysis and is more relevant for classification (Du et al ).…”

Section: Evaluation Of Existing Classification Approachesmentioning

confidence: 99%

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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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Section: Associated Pre‐processing Techniques and Instrumentsmentioning

confidence: 99%

Section: Evaluation Of Existing Classification Approachesmentioning

confidence: 99%

Hyperspectral Remote Sensing Classifications: A Perspective Survey

Chutia

Bhattacharyya

Sarma

et al. 2015

Transactions in GIS

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“…Minimum noise fraction eigenvectors 1, 3-6 and 10 were exported to ArcMap for further classification processing since their eigenvalues were greater than five (similarly to e.g. Harris et al, 2006), and were visually coherent. Normalized differential vegetation index (NDVI) was also produced (red = band 6, 572.86-580.12 nm; NIR = band 13, 776.83-784.42 nm).…”

Section: Neural Network Classification and Accuracy Assessmentmentioning

confidence: 99%

Imaging spectroscopy in soil-water based site suitability assessment for artificial regeneration to Scots pine

Middleton¹,

Närhi²,

Sutinen³

2011

ISPRS Journal of Photogrammetry and Remote Sensing

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“…One possible solution for mitigating the effects of the Hughes phenomenon is to reduce the dimensionality of the data but at the same time keep as much information as possible. For example, commonly used dimensionality reduction methods include feature selection and feature extraction methods [ 5 - 9 ], principal components analysis (PCA) with conventional classification methods [ 10 ], Minimum Noise Fraction [ 11 ], orthogonal subspace projection classification methods [ 12 ], support vector machine (SVM) classifiers [ 13 - 18 ], and spectral angle mapper and spectral information divergence methods [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Extended Averaged Learning Subspace Method for Hyperspectral Data Classification

Bagan

Takeuchi

Yamagata

et al. 2009

Sensors

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Averaged learning subspace methods (ALSM) have the advantage of being easily implemented and appear to outperform in classification problems of hyperspectral images. However, there remain some open and challenging problems, which if addressed, could further improve their performance in terms of classification accuracy. We carried out experiments mainly by using two kinds of improved subspace methods (namely, dynamic and fixed subspace methods), in conjunction with the [0,1] and [-1,+1] normalization methods. We used different performance indicators to support our experimental studies: classification accuracy, computation time, and the stability of the parameter settings. Results are presented for the AVIRIS Indian Pines data set. Experimental analysis showed that the fixed subspace method combined with the [0,1] normalization method yielded higher classification accuracy than other subspace methods. Moreover, ALSMs are easily applied: only two parameters need to be set, and they can be applied directly to hyperspectral data. In addition, they can completely identify training samples in a finite number of iterations.

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Noise reduction and best band selection techniques for improving classification results using hyperspectral data: application to lithological mapping in Canada's Arctic

Cited by 14 publications

References 8 publications

Hyperspectral Remote Sensing Classifications: A Perspective Survey

Hyperspectral Remote Sensing Classifications: A Perspective Survey

Imaging spectroscopy in soil-water based site suitability assessment for artificial regeneration to Scots pine

Extended Averaged Learning Subspace Method for Hyperspectral Data Classification

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