Compression of multispectral remote sensing images using clustering and spectral reduction

Kaarna, Arto; Zemčík, Pavel; Kälviäinen, Heikki; Parkkinen, Jussi

doi:10.1109/36.841986

Cited by 59 publications

(42 citation statements)

References 15 publications

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“…Similar results have been reported for sensors operating in the reflective regions of the spectrum. [5][6][7][8][9] Our results show utility in the thermal IR region. Images of the ICA output for the extracted components show qualitative differences from PCA in the sense that individual objects are highlighted separately, at least in some cases.…”

Section: Discussionmentioning

confidence: 73%

“…Whether they are more useful than PCA components remains to be seen. Variants of ICA that have been described by others 5,8 may be useful in thermal IR imagery where independence of the data is not completely satisfied.…”

Section: Discussionmentioning

confidence: 99%

“…3,4 ICA has previously been applied to hyperspectral and multispectral remote sensing problems in the vis-SWIR spectral regions. [5][6][7][8] It has also been implemented in chemical analysis and quantification for complex mixtures in analytical chemistry applications. 9 We have a particular interest in remote chemical detection using both passive and active (lidar) sensors in the thermal IR.…”

Section: Introductionmentioning

confidence: 99%

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Scene analysis and detection in thermal infrared remote sensing using independent component analysis

Foy

Theiler

2004

SPIE Proceedings

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Independent Component Analysis can be used to analyze cluttered scenes from remote sensing imagery and to detect objects. We show examples in the thermal infrared spectral region (8-12 µm) using both passive hyperspectral data and active multispectral data. The examples are from actual field data and computer simulations. ICA isolates spectrally distinct objects with nearly one-to-one correspondence with the independent component basis functions, making it useful for modeling the clutter in typical scenes. We show examples of chemical plume detection in real and simulated data.

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Section: Discussionmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Scene analysis and detection in thermal infrared remote sensing using independent component analysis

Foy

Theiler

2004

SPIE Proceedings

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“…Karena adanya korelasi yang tinggi tersebut, maka pada sebuah citra multispektral selalu terdapat redundansi informasi. Proses clustering dapat digunakan untuk mengurangi redundansi tersebut [3]. Tetapi ternyata dari hasil proses clustering masih terdapat redundansi informasi.…”

Section: Pendahuluanunclassified

“…Tetapi ternyata dari hasil proses clustering masih terdapat redundansi informasi. Pada penelitian yang telah ada [3], proses clustering ini dikombinasikan dengan reduksi spektral untuk meminimalkan redundansi informasi.…”

Section: Pendahuluanunclassified

Kompresi Citra Penginderaan Jauh Multispektral Berbasis Clustering Dan Reduksi Spektral

Arifin¹,

Lestriandoko²

2003

JUTI

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Spectral compression using subspace clustering

Agahian

Funt

2015

Color Research & Application

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This article describes a subspace clustering strategy for the spectral compression of multispectral images. Unlike standard principal component analysis, this approach finds clusters in several different subspaces of different dimension. Consequently, instead of representing all spectra in a single low‐dimensional subspace of a fixed dimension, spectral data are assigned to multiple subspaces having a range of dimensions from one to eight. In other words, this strategy allows us to distribute spectra into different subspaces thereby obtaining the best fit for each. As a result, more resources can be allocated to those spectra that need many dimensions for accurate representation and fewer resources to those that can be modeled using fewer dimensions. For a given compression ratio, this trade off reduces the overall reconstruction error. In the case of compressing multispectral images, this initial compression method is followed by JPEG2000 compression in order to remove the spatial redundancy in the data as well. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 7–15, 2016

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Compression of multispectral remote sensing images using clustering and spectral reduction

Cited by 59 publications

References 15 publications

Scene analysis and detection in thermal infrared remote sensing using independent component analysis

Scene analysis and detection in thermal infrared remote sensing using independent component analysis

Kompresi Citra Penginderaan Jauh Multispektral Berbasis Clustering Dan Reduksi Spektral

Spectral compression using subspace clustering

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