Efficient transmission and classification of hyperspectral image data

Xiuping Jia; John A. Richards

doi:10.1109/TGRS.2003.810710

Efficient transmission and classification of hyperspectral image data

Xiuping Jia^*, John A. Richards

^*Corresponding author for this work

Research output: Contribution to journal › Letter › peer-review

16 Citations (Scopus)

Abstract

An extension of a newly developed cluster-space representation is applied to efficient data transmission and classification. Cluster-space classification, which is an automatic hybrid supervised and unsupervised classification procedure, can be performed in two stages. A "semiproduct" with low entropy is generated at the sender end. It is then transmitted to a range of users for further classification. Experiments using a HyMap dataset demonstrate the advantages in data transmission and the satisfactory classification accuracy.

Original language	English
Pages (from-to)	1129-1131
Number of pages	3
Journal	IEEE Transactions on Geoscience and Remote Sensing
Volume	41
Issue number	5 PART II
DOIs	https://doi.org/10.1109/TGRS.2003.810710
Publication status	Published - May 2003

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10.1109/TGRS.2003.810710

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title = "Efficient transmission and classification of hyperspectral image data",

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keywords = "Clustering classification, Hyperspectral, Transmission",

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AU - Jia, Xiuping

AU - Richards, John A.

PY - 2003/5

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N2 - An extension of a newly developed cluster-space representation is applied to efficient data transmission and classification. Cluster-space classification, which is an automatic hybrid supervised and unsupervised classification procedure, can be performed in two stages. A "semiproduct" with low entropy is generated at the sender end. It is then transmitted to a range of users for further classification. Experiments using a HyMap dataset demonstrate the advantages in data transmission and the satisfactory classification accuracy.

AB - An extension of a newly developed cluster-space representation is applied to efficient data transmission and classification. Cluster-space classification, which is an automatic hybrid supervised and unsupervised classification procedure, can be performed in two stages. A "semiproduct" with low entropy is generated at the sender end. It is then transmitted to a range of users for further classification. Experiments using a HyMap dataset demonstrate the advantages in data transmission and the satisfactory classification accuracy.

KW - Clustering classification

KW - Hyperspectral

KW - Transmission

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U2 - 10.1109/TGRS.2003.810710

DO - 10.1109/TGRS.2003.810710

M3 - Letter

SN - 0196-2892

VL - 41

SP - 1129

EP - 1131

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

IS - 5 PART II

ER -

Efficient transmission and classification of hyperspectral image data

Abstract

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