Remote sensing image fusion using best bases sparse representation

Mahboob Iqbal; Jie Chen; Xian Zhong Wen; Chun Sheng Li

doi:10.1109/IGARSS.2012.6352378

Remote sensing image fusion using best bases sparse representation

Mahboob Iqbal^*
, Jie Chen
, Xian Zhong Wen
, Chun Sheng Li

^*Corresponding author for this work

School of Electronic and Information Engineering

Beihang University

Research output: Contribution to conference › Paper › peer-review

8 Scopus citations

Abstract

A new technique based on best bases sparse representation is proposed for fusion of remote sensing images. In order to carry out multi-resolution image fusion, low-resolution image is upscaled to match resolution of high-resolution images. Corresponding patches from remote sensing images are represented by finding out best bases from over-complete dictionaries comprising of elements derived from basis function of DCT, Wavelets, ridgelets, and curvelets. The corresponding bases of image patches are combined based on local information parameter (LIP) derived from respective patches. The use of LIP helps ensure transfer of details in high-resolution image into fused image.

Original language	English
Pages	5430-5433
Number of pages	4
DOIs	https://doi.org/10.1109/IGARSS.2012.6352378
State	Published - 2012
Event	2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 - Munich, Germany Duration: 22 Jul 2012 → 27 Jul 2012

Conference

Conference	2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012
Country/Territory	Germany
City	Munich
Period	22/07/12 → 27/07/12

Keywords

Fusion
Over-complete Dictionaries
Pan-sharpening
Remote Sensing
Sparse Representation

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10.1109/IGARSS.2012.6352378

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title = "Remote sensing image fusion using best bases sparse representation",

abstract = "A new technique based on best bases sparse representation is proposed for fusion of remote sensing images. In order to carry out multi-resolution image fusion, low-resolution image is upscaled to match resolution of high-resolution images. Corresponding patches from remote sensing images are represented by finding out best bases from over-complete dictionaries comprising of elements derived from basis function of DCT, Wavelets, ridgelets, and curvelets. The corresponding bases of image patches are combined based on local information parameter (LIP) derived from respective patches. The use of LIP helps ensure transfer of details in high-resolution image into fused image.",

keywords = "Fusion, Over-complete Dictionaries, Pan-sharpening, Remote Sensing, Sparse Representation",

author = "Mahboob Iqbal and Jie Chen and Wen, \{Xian Zhong\} and Li, \{Chun Sheng\}",

year = "2012",

doi = "10.1109/IGARSS.2012.6352378",

language = "英语",

pages = "5430--5433",

note = "2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 ; Conference date: 22-07-2012 Through 27-07-2012",

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TY - CONF

T1 - Remote sensing image fusion using best bases sparse representation

AU - Iqbal, Mahboob

AU - Chen, Jie

AU - Wen, Xian Zhong

AU - Li, Chun Sheng

PY - 2012

Y1 - 2012

N2 - A new technique based on best bases sparse representation is proposed for fusion of remote sensing images. In order to carry out multi-resolution image fusion, low-resolution image is upscaled to match resolution of high-resolution images. Corresponding patches from remote sensing images are represented by finding out best bases from over-complete dictionaries comprising of elements derived from basis function of DCT, Wavelets, ridgelets, and curvelets. The corresponding bases of image patches are combined based on local information parameter (LIP) derived from respective patches. The use of LIP helps ensure transfer of details in high-resolution image into fused image.

AB - A new technique based on best bases sparse representation is proposed for fusion of remote sensing images. In order to carry out multi-resolution image fusion, low-resolution image is upscaled to match resolution of high-resolution images. Corresponding patches from remote sensing images are represented by finding out best bases from over-complete dictionaries comprising of elements derived from basis function of DCT, Wavelets, ridgelets, and curvelets. The corresponding bases of image patches are combined based on local information parameter (LIP) derived from respective patches. The use of LIP helps ensure transfer of details in high-resolution image into fused image.

KW - Fusion

KW - Over-complete Dictionaries

KW - Pan-sharpening

KW - Remote Sensing

KW - Sparse Representation

UR - https://www.scopus.com/pages/publications/84873157602

U2 - 10.1109/IGARSS.2012.6352378

DO - 10.1109/IGARSS.2012.6352378

M3 - 论文

AN - SCOPUS:84873157602

SP - 5430

EP - 5433

T2 - 2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012

Y2 - 22 July 2012 through 27 July 2012

ER -

Remote sensing image fusion using best bases sparse representation

Abstract

Conference

Keywords

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