R&D activities in airborne SAR image processing/analysis at Lockheed Martin Canada

1998 ◽  
Author(s):  
Langis Gagnon ◽  
H. Oppenheim ◽  
Pierre Valin
Keyword(s):  
Image Processing ◽  
Sar Image ◽  
Airborne Sar

Hybrid Laplacian Gaussian Based Speckle Removal in SAR Image Processing

2019 ◽  
Vol 43 (7) ◽  
Author(s):  
A. Glory Sujitha ◽  
Dr. P. Vasuki ◽  
A. Amala Deepan
Keyword(s):  
Image Processing ◽  
Sar Image

Textural Filtering For SAR Image Processing

1990 ◽  
Vol 28 (4) ◽  
pp. 735-737 ◽  
Author(s):  
Li Wang ◽  
Dong-chen He ◽  
A. Fabbri
Keyword(s):  
Image Processing ◽  
Sar Image

L-band SAR image processing for the determination of coastal bathymetry based on swell analysis

2014 ◽  
Author(s):  
Valentina Boccia ◽  
Alfredo Renga ◽  
Giancarlo Rufino ◽  
Antonio Moccia ◽  
Marco D'Errico ◽  
...  
Keyword(s):  
Image Processing ◽  
Sar Image ◽  
L Band

scholarly journals Modifications of Range-Doppler Algorithm for Compensation of SAR Platform Motion Instabilities

2014 ◽  
Vol 60 (3) ◽  
pp. 225-231 ◽  
Author(s):  
Ievgen M. Gorovyi ◽  
Oleksandr O. Bezvesilniy ◽  
Dmytro M. Vavriv
Keyword(s):  
Experimental Data ◽  
Image Formation ◽  
Sar Image ◽  
Quadratic Map ◽  
Platform Motion ◽  
Airborne Sar ◽  
Short Time ◽  
Time Acquisition

Abstract Two modifications of the range-Doppler algorithm (RDA) have been proposed to solve problems of SAR platform motion instabilities. First, the multi-look processing based on the RDA with an extended Doppler bandwidth has been introduced for correction of radiometric errors. Second, the RDA has been modified to perform SAR image formation on short-time acquisition intervals to use it in a recently-developed local-quadratic map-drift autofocus (LQMDA) method. The performance of the methods is illustrated with experimental data obtained by airborne SAR systems.

A geometric preprocessing model for airborne SAR image

2006 ◽  
Author(s):  
Dan Lu ◽  
Guoman Huang ◽  
Zheng Zhao
Keyword(s):  
Sar Image ◽  
Airborne Sar

Estimation of qth-Order Fractal Dimensions

Fractals ◽  
1997 ◽  
Vol 05 (supp01) ◽  
pp. 257-269 ◽  
Author(s):  
Stefano Fioravanti ◽  
Daniele D. Giusto
Keyword(s):  
Image Processing ◽  
Fractal Dimension ◽  
Texture Analysis ◽  
Digital Image Processing ◽  
Digital Image ◽  
State Of The Art ◽  
Fractal Dimensions ◽  
Sar Image ◽  
Spectrum Function ◽  
Novel Algorithm

The paper deals with the theory of qth-order fractal dimensions and its application to texture analysis. In particular, the state-of-the-art regarding the fractal dimension estimation for characterizing textures is presented. After, the insufficiency of the single fractal dimension is proven and the qth order fractal dimensions are introduced to overcome such drawback. The multifractality spectrum function D(q) is described, a novel algorithm for estimating such dimensions is then proposed, and its use in digital-image processing is addressed. Results on real SAR image textures are reported and discussed.

scholarly journals Similarity measures of full polarimetric SAR images fusion for improved SAR image matching

2015 ◽  
Vol XL-7/W4 ◽  
pp. 13-16 ◽  
Author(s):  
H. Ding
Keyword(s):  
Similarity Measure ◽  
Image Matching ◽  
Stereo Matching ◽  
Similarity Measures ◽  
Sar Image ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Sar Data ◽  
Sar Imagery ◽  
Airborne Sar

China’s first airborne SAR mapping system (CASMSAR) developed by Chinese Academy of Surveying and Mapping can acquire high-resolution and full polarimetric (HH, HV, VH and VV) Synthetic aperture radar (SAR) data. It has the ability to acquire X-band full polarimetric SAR data at a resolution of 0.5m. However, the existence of speckles which is inherent in SAR imagery affects visual interpretation and image processing badly, and challenges the assumption that conjugate points appear similar to each other in matching processing. In addition, researches show that speckles are multiplicative speckles, and most similarity measures of SAR image matching are sensitive to them. Thus, matching outcomes of SAR images acquired by most similarity measures are not reliable and with bad accuracy. Meanwhile, every polarimetric SAR image has different backscattering information of objects from each other and four polarimetric SAR data contain most basic and a large amount of redundancy information to improve matching. Therefore, we introduced logarithmically transformation and a stereo matching similarity measure into airborne full polarimetric SAR imagery. Firstly, in order to transform the multiplicative speckles into additivity ones and weaken speckles' influence on similarity measure, logarithmically transformation have to be taken to all images. Secondly, to prevent performance degradation of similarity measure caused by speckles, measure must be free or insensitive of additivity speckles. Thus, we introduced a stereo matching similarity measure, called Normalized Cross-Correlation (NCC), into full polarimetric SAR image matching. Thirdly, to take advantage of multi-polarimetric data and preserve the best similarity measure value, four measure values calculated between left and right single polarimetric SAR images are fused as final measure value for matching. The method was tested for matching under CASMSAR data. The results showed that the method delivered an effective performance on experimental imagery and can be used for airborne SAR matching applications.

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