scholarly journals Fusion Method for Remote Sensing Image Based on Fuzzy Integral

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hui Zhou ◽  
Hongmin Gao
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
Wavelet Decomposition ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Weighting Coefficient ◽  
Spectral Information ◽  
Fuzzy Integral ◽  
Fusion Method

This paper presents a kind of image fusion method based on fuzzy integral, integrated spectral information, and 2 single factor indexes of spatial resolution in order to greatly retain spectral information and spatial resolution information in fusion of multispectral and high-resolution remote sensing images. Firstly, wavelet decomposition is carried out to two images, respectively, to obtain wavelet decomposition coefficients of the two image and keep coefficient of low frequency of multispectral image, and then optimized fusion is carried out to high frequency part of the two images based on weighting coefficient to generate new fusion image. Finally, evaluation is carried out to the image after fusion with introduction of evaluation indexes of correlation coefficient, mean value of image, standard deviation, distortion degree, information entropy, and so forth. The test results show that this method integrated multispectral information and space high-resolution information in a better way, and it is an effective fusion method of remote sensing image.

Multi-Spectral Image Fusion Using Intensity Modulation and Non-Separable Wavelet Frame

2005 ◽  
Author(s):  
Xuhong Yang ◽  
Zhongliang Jing ◽  
Jian-Xun Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
Intensity Modulation ◽  
Spectral Information ◽  
Wavelet Frame ◽  
Remote Sensing Images ◽  
Spectral Image ◽  
Panchromatic Image ◽  
Fusion Approach

A fusion approach is proposed to refine the resolution of multi-spectral images using the corresponding high-resolution panchromatic images. The technique is based on intensity modulation and non-separable wavelet frame. The high-resolution panchromatic image is decomposed by the non-separable wavelet frame. Then the wavelet coefficients are used as the factor of modulating to modulate the multi-spectral image. Experimental results indicate that, comparing with the traditional methods, the proposed method can efficiently preserve the spectral information while improving the spatial resolution of remote sensing images.

scholarly journals RESEARCH ON HORIZONTAL ACCURACY METHOD OF HIGH SPATIAL RESOLUTION REMOTELY SENSED ORTHOPHOTO IMAGE

2018 ◽  
Vol XLII-3 ◽  
pp. 2005-2007
Author(s):  
Y. M. Xu ◽  
J. X. Zhang ◽  
F. Yu ◽  
S. Dong
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
Field Measurement ◽  
Reference Data ◽  
High Spatial Resolution ◽  
Remote Sensing Image ◽  
High Accuracy ◽  
New Method ◽  
Orthophoto Images

At present, in the inspection and acceptance of high spatial resolution remotly sensed orthophoto image, the horizontal accuracy detection is testing and evaluating the accuracy of images, which mostly based on a set of testing points with the same accuracy and reliability. However, it is difficult to get a set of testing points with the same accuracy and reliability in the areas where the field measurement is difficult and the reference data with high accuracy is not enough. So it is difficult to test and evaluate the horizontal accuracy of the orthophoto image. The uncertainty of the horizontal accuracy has become a bottleneck for the application of satellite borne high-resolution remote sensing image and the scope of service expansion. Therefore, this paper proposes a new method to test the horizontal accuracy of orthophoto image. This method using the testing points with different accuracy and reliability. These points’ source is high accuracy reference data and field measurement. The new method solves the horizontal accuracy detection of the orthophoto image in the difficult areas and provides the basis for providing reliable orthophoto images to the users.

scholarly journals REMOTE SENSING IMAGE FUSION USING ICA AND OPTIMIZED WAVELET TRANSFORM

2016 ◽  
Vol XLI-B7 ◽  
pp. 653-659
Author(s):  
V. V. Hnatushenko ◽  
V. V. Vasyliev
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
High Resolution ◽  
Image Fusion ◽  
Wavelet Decomposition ◽  
Performance Metrics ◽  
Spectral Characteristics ◽  
Remote Sensing Image ◽  
Remote Sensing Image Fusion ◽  
Spectral Channels

In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines the Independent Component Analysis (ICA) and optimization wavelet transform. The proposed method is based on selection of multiscale components obtained after the ICA of images on the base of their wavelet decomposition and formation of linear forms detailing coefficients of the wavelet decomposition of images brightness distributions by spectral channels with iteratively adjusted weights. These coefficients are determined as a result of solving an optimization problem for the criterion of maximization of information entropy of the synthesized images formed by means of wavelet reconstruction. Further, reconstruction of the images of spectral channels is done by the reverse wavelet transform and formation of the resulting image by superposition of the obtained images. To verify the validity, the new proposed method is compared with several techniques using WorldView-2 satellite data in subjective and objective aspects. In experiments we demonstrated that our scheme provides good spectral quality and efficiency. Spectral and spatial quality metrics in terms of RASE, RMSE, CC, ERGAS and SSIM are used in our experiments. These synthesized MS images differ by showing a better contrast and clarity on the boundaries of the "object of interest - the background". The results show that the proposed approach performs better than some compared methods according to the performance metrics.

scholarly journals REMOTE SENSING IMAGE FUSION USING ICA AND OPTIMIZED WAVELET TRANSFORM

2016 ◽  
Vol XLI-B7 ◽  
pp. 653-659 ◽  
Author(s):  
V. V. Hnatushenko ◽  
V. V. Vasyliev
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
High Resolution ◽  
Image Fusion ◽  
Wavelet Decomposition ◽  
Performance Metrics ◽  
Spectral Characteristics ◽  
Remote Sensing Image ◽  
Remote Sensing Image Fusion ◽  
Spectral Channels

In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines the Independent Component Analysis (ICA) and optimization wavelet transform. The proposed method is based on selection of multiscale components obtained after the ICA of images on the base of their wavelet decomposition and formation of linear forms detailing coefficients of the wavelet decomposition of images brightness distributions by spectral channels with iteratively adjusted weights. These coefficients are determined as a result of solving an optimization problem for the criterion of maximization of information entropy of the synthesized images formed by means of wavelet reconstruction. Further, reconstruction of the images of spectral channels is done by the reverse wavelet transform and formation of the resulting image by superposition of the obtained images. To verify the validity, the new proposed method is compared with several techniques using WorldView-2 satellite data in subjective and objective aspects. In experiments we demonstrated that our scheme provides good spectral quality and efficiency. Spectral and spatial quality metrics in terms of RASE, RMSE, CC, ERGAS and SSIM are used in our experiments. These synthesized MS images differ by showing a better contrast and clarity on the boundaries of the "object of interest - the background". The results show that the proposed approach performs better than some compared methods according to the performance metrics.

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