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Proceedings Paper

Kernel adaptive filter (SRSSHF) and quality improvement method for hyperspectral imaging based on spectral dimension recognition and spatial dimension smoothing according to CSAM
Author(s): Yongchao Zhao; Qingxi Tong; Lanfen Zheng; Bing Zhang; Xia Zhang; Jiwei Bai; Chuanqing Wu; Tuanjie Liu
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Paper Abstract

According to the advanced feature of hyperspectral image and Correlation Simulating Analysis Model (CSAM), a new simple but efficient kernel-adaptive filter (SRSSHF) especially for hyperspectral image is suggested in this paper. It is achieved not based on the traditional sigma (standard deviation) statistics in spatial dimension, but on the valid-pixel judge in spectral dimension and the intellectualized shift convolution in spatial dimensions. So its criteria is based on the intrinsic property of objects by adequately utilizing the spectral information that hyperspectral affords. Such a filter also is an adaptive filter, and its kernel size theoretically has no strong influence on the filter results. What it concentrates is the feature of signal itself but not the speckle noise, its criterion is in spectral dimension, and multiple iteration is available. So the tradeoff of spatial texture is not necessary. It is applied to filter and improve quality of PHI hyperspectral images acquired both in Changzhou, China and Nagano, Japan, and a >200 looks iteration and a comparison with other typical adaptive filters also are tried. It shows that SRSSHF can smooth whole the internal of a homogeneous area while ideally keep and, as well as, enhance the edges well. As good results are achieved, this paper suggests that SRSSHF on the base of CSAM is a relative ideal filter for HRS images. Some other features of SRSSHF are also discussed in this paper.

Paper Details

Date Published: 20 September 2001
PDF: 7 pages
Proc. SPIE 4552, Image Matching and Analysis, (20 September 2001); doi: 10.1117/12.441522
Show Author Affiliations
Yongchao Zhao, Institute of Remote Sensing Applications (China)
Qingxi Tong, Institute of Remote Sensing Applications (China)
Lanfen Zheng, Institute of Remote Sensing Applications (China)
Bing Zhang, Institute of Remote Sensing Applications (China)
Xia Zhang, Institute of Remote Sensing Applications (China)
Jiwei Bai, Institute of Remote Sensing Applications (China)
Chuanqing Wu, Institute of Remote Sensing Applications (China)
Tuanjie Liu, Institute of Remote Sensing Applications (China)


Published in SPIE Proceedings Vol. 4552:
Image Matching and Analysis

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