Remote sensing image denoising by using discrete multiwavelet transform techniques

2006 ◽  
Author(s):  
Haihui Wang ◽  
Jun Wang ◽  
Jian Zhang
Keyword(s):  
Remote Sensing ◽  
Image Denoising ◽  
Remote Sensing Image ◽  
Multiwavelet Transform ◽  
Discrete Multiwavelet Transform

The Research of Remote Sensing Image Fusion Technology

2014 ◽  
Vol 513-517 ◽  
pp. 3237-3240
Author(s):  
Bin Wang ◽  
Chao Wang
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
Image Fusion ◽  
Remote Sensing Image ◽  
Fusion Method ◽  
Fusion Technology ◽  
Remote Sensing Image Fusion ◽  
Multiwavelet Transform ◽  
Full Analysis ◽  
Discrete Multiwavelet Transform

Remote sensing image fusion is an important branch in the field of image fusion. Remote sensing image fusion mainly researches how to use different aerial remote sensor to obtain relevant image information. In view of the present research status in this field and the full analysis of remote sensing image fusion method and wavelet transform, the paper aims at proposing the image fusion technology of combining discrete multiwavelet transform and HIS method, then the paper analyses the experimental results with the help of MATLAB. It is proved that the analysis method of wavelet has more advantages than traditional methods: Wavelet transform fusion method can keep maximized resolution and multispectral image.

Dual-Complementary Convolution Network for Remote-Sensing Image Denoising

2021 ◽  
pp. 1-5
Author(s):  
Xinlei Jia ◽  
Yali Peng ◽  
Jun Li ◽  
Bao Ge ◽  
Yunhong Xin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Denoising ◽  
Remote Sensing Image

A method for remote sensing image denoising based on structured dictionary learning and cloud computing

2020 ◽  
pp. 002072092093107
Author(s):  
Ru Yang ◽  
Zhentao Qin ◽  
Xiangyu Zhao
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Image Denoising ◽  
Dictionary Learning ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Multispectral Image ◽  
Structured Dictionary

With the emerging technology of remote sensing, a huge amount of remote sensing data is collected and stored in the remote sensin02222g platform, and the transmission and processing of data on the platform is extremely wasteful. It is essential to incorporate the speedy remote sensing processing services in an integrated cloud computing architecture. In order to improve the denoising ability of remote sensing image, a new structured dictionary-based method for multispectral image denoising based on cluster is proposed. This method incorporates both the locality of spatial and the correlation across spectrum of multispectral image. Remote sensing image is divided into different groups by clustering, and sparse representation coefficients of spatial and spectral and dictionary is obtained according to the dictionary learning algorithm. After threshold processing, the similar blocks are averaged and realized with multispectral remote sensing image denoising. The algorithm is applied to denoise the noisy remote sensing image of Maoergai area in the upper Minjiang which contain typical vegetation and soil is chosen as study area, simulation results show that higher peak-signal to noise ratio can be obtained as compared to other recent image denoising methods.

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