A Segmentation-Cooperated Pansharpening Method Using Local Adaptive Spectral Modulation
In order to improve the spatial resolution of multispectral (MS) images and reduce spectral distortion, a segmentation-cooperated pansharpening method using local adaptive spectral modulation (LASM) is proposed in this paper. By using the k-means algorithm for the segmentation of MS images, differen...
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doaj-1019fdc6486d4596be78fc9f76dd8bea2020-11-25T00:26:21ZengMDPI AGElectronics2079-92922019-06-018668510.3390/electronics8060685electronics8060685A Segmentation-Cooperated Pansharpening Method Using Local Adaptive Spectral ModulationJiao Jiao0Lingda Wu1Kechang Qian2Science and Technology on Complex Electronic System Simulation Laboratory, Space Engineering University, Beijing 101416, ChinaScience and Technology on Complex Electronic System Simulation Laboratory, Space Engineering University, Beijing 101416, ChinaSchool of Space Information, Space Engineering University, Beijing 101416, ChinaIn order to improve the spatial resolution of multispectral (MS) images and reduce spectral distortion, a segmentation-cooperated pansharpening method using local adaptive spectral modulation (LASM) is proposed in this paper. By using the k-means algorithm for the segmentation of MS images, different connected component groups can be obtained according to their spectral characteristics. For spectral information modulation of fusion images, the LASM coefficients are constructed based on details extracted from images and local spectral relationships among MS bands. Moreover, we introduce a cooperative theory for the pansharpening process. The local injection coefficient matrix and LASM coefficient matrix are estimated based on the connected component groups to optimize the fusion result, and the parameters of the segmentation algorithm are adjusted according to the feedback from the pansharpening result. In the experimental part, degraded and real data sets from GeoEye-1 and QuickBird satellites are used to assess the performance of our proposed method. Experimental results demonstrate the validity and effectiveness of our method. Generally, the method is superior to several classic and state-of-the-art pansharpening methods in both subjective visual effect and objective evaluation indices, achieving a balance between the injection of spatial details and maintenance of spectral information, while effectively reducing the spectral distortion of the fusion image.https://www.mdpi.com/2079-9292/8/6/685pansharpeningcooperation with segmentationlocal adaptive spectral modulationk-means algorithm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jiao Jiao Lingda Wu Kechang Qian |
spellingShingle |
Jiao Jiao Lingda Wu Kechang Qian A Segmentation-Cooperated Pansharpening Method Using Local Adaptive Spectral Modulation Electronics pansharpening cooperation with segmentation local adaptive spectral modulation k-means algorithm |
author_facet |
Jiao Jiao Lingda Wu Kechang Qian |
author_sort |
Jiao Jiao |
title |
A Segmentation-Cooperated Pansharpening Method Using Local Adaptive Spectral Modulation |
title_short |
A Segmentation-Cooperated Pansharpening Method Using Local Adaptive Spectral Modulation |
title_full |
A Segmentation-Cooperated Pansharpening Method Using Local Adaptive Spectral Modulation |
title_fullStr |
A Segmentation-Cooperated Pansharpening Method Using Local Adaptive Spectral Modulation |
title_full_unstemmed |
A Segmentation-Cooperated Pansharpening Method Using Local Adaptive Spectral Modulation |
title_sort |
segmentation-cooperated pansharpening method using local adaptive spectral modulation |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2019-06-01 |
description |
In order to improve the spatial resolution of multispectral (MS) images and reduce spectral distortion, a segmentation-cooperated pansharpening method using local adaptive spectral modulation (LASM) is proposed in this paper. By using the k-means algorithm for the segmentation of MS images, different connected component groups can be obtained according to their spectral characteristics. For spectral information modulation of fusion images, the LASM coefficients are constructed based on details extracted from images and local spectral relationships among MS bands. Moreover, we introduce a cooperative theory for the pansharpening process. The local injection coefficient matrix and LASM coefficient matrix are estimated based on the connected component groups to optimize the fusion result, and the parameters of the segmentation algorithm are adjusted according to the feedback from the pansharpening result. In the experimental part, degraded and real data sets from GeoEye-1 and QuickBird satellites are used to assess the performance of our proposed method. Experimental results demonstrate the validity and effectiveness of our method. Generally, the method is superior to several classic and state-of-the-art pansharpening methods in both subjective visual effect and objective evaluation indices, achieving a balance between the injection of spatial details and maintenance of spectral information, while effectively reducing the spectral distortion of the fusion image. |
topic |
pansharpening cooperation with segmentation local adaptive spectral modulation k-means algorithm |
url |
https://www.mdpi.com/2079-9292/8/6/685 |
work_keys_str_mv |
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