Lossy compression of multispectral remote-sensing images through multiresolution data fusion techniques

2003 ◽  
Author(s):  
Bruno Aiazzi ◽  
Luciano Alparone ◽  
Stefano Baronti ◽  
Massimo Selva
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Lossy Compression ◽  
Remote Sensing Images ◽  
Multispectral Remote Sensing

OPTIMAL OPERATION POINT IN 3D DCT- BASED LOSSY COMPRESSION OF COLOR AND MULTICHANNEL REMOTE SENSING IMAGES

2015 ◽  
Vol 74 (20) ◽  
pp. 1803-1821 ◽  
Author(s):  
V. V. Lukin ◽  
S. K. Abramov ◽  
R.A. Kozhemiakin ◽  
Benoit Vozel ◽  
B. Djurovic ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Lossy Compression ◽  
Optimal Operation ◽  
Remote Sensing Images

scholarly journals Curvelet Transform based Denoising of Multispectral Remote Sensing Images

2021 ◽  
Vol 2089 (1) ◽  
pp. 012064
Author(s):  
P. Lokeshwara Reddy ◽  
Santosh Pawar ◽  
S.L. Prathapa Reddy
Keyword(s):  
Remote Sensing ◽  
Signal To Noise Ratio ◽  
Curvelet Transform ◽  
Structural Similarity ◽  
Spatial Location ◽  
Multispectral Image ◽  
Sensor Technology ◽  
Remote Sensing Images ◽  
Unequally Spaced ◽  
Multispectral Remote Sensing

Abstract With the advent of sensor technology, the exertion of multispectral image (MSI) is comely omnipresent. Denoising is an essential quest in multispectral image processing which further improves recital of unmixing, classification and supplementary ensuing praxis. Explication and ocular analysis are essential to extricate data from remote sensing images for broad realm of supplications. This paper describes curvelet transform based denoising of multispectral remote sensing images. The implementation of curvelet transform is done by using both wrapping function and unequally spaced fast Fourier transform (USFFT) and they diverge in selection of spatial grid which is used to construe curvelets at every orientation and scale. The coefficients of curvelets are docket by a scaling factor, angle and spatial location criterion. This paper crisps on denoising of Linear Imaging Self Scanning Sensor (LISS) III images. The proposed denoising approach has also been collated with some existing schemes for assessment. The efficacy of proposed approach is analyzed with calculation of facet matrices such as Peak signal to noise ratio and Structural similarity at distinct variance of noise..

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