XVIIIth Congress: XVIIIth Congress

Kraus, Karl; Waldhäusl, Peter

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Multivolume work

Persistent identifier:: 1667435949

Title:: XVIIIth Congress

Sub title:: Vienna, Austria 1996

Year of publication:: 1996

Place of publication:: Vienna

Publisher of the original:: Austrian Society of Surveying and Geoinformation

Identifier (digital):: 1667435949

Language:: English

Editor:: Kraus, Karl; Waldhäusl, Peter

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1667438379

Title:: XVIIIth Congress

Scope:: 449 Seiten

Year of publication:: 1996

Place of publication:: Vienna

Publisher of the original:: Austrian Society of Surveying and Geoinformation

Identifier (digital):: 1667438379

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(31,B2)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist anhand des Copyrightjahrs ermittelt.

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Kraus, Karl; Waldhäusl, Peter

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: SAR SPECKLE SIMULATION Regine Bolter, Margrit Gelautz, Franz Leberl

Document type:: Multivolume work

Structure type:: Chapter

fferent d, and nd the bution tischen ieben nd von vie der square se mit image zimuth to sta- ]. The ayleigh ibuted, ive ex- N non- ion for m. As stribu- inguish ors. In sensor an ap- image. > phys- 1g into a high üins in- image ing ge- image peckle its sta- 'adows iniques we de- d algo- g SAR image simulator. The results obtained from application to ERS-1 images are presented and evaluated by visual compar- ison with the corresponding real image. 2 SPECKLE SIMULATION METHODS Whereas a considerable body of literature deals with SAR speckle filtering, the topic of SAR speckle simulation can only be found in a limited number of publications. An early work suited to the analog representation of radar images on film was published by [Holtzman, 1978]. In 1978, when this paper was published, the intensity of the video sig- nal exiting the receiver was recorded on film, and this process is modeled by the simulator. The starting point for the radar simulation imaging model (gray tone equation) is the pre- diction of the power reflected from each resolvable ground element (resolution cell). It is assumed that the ground can be modeled as a collection of homogeneous regions, each at least the size of a resolution cell. After the ground truth data base (terrain feature model) of the described site has been specified, the reflectivity data for the various categories included in the data base have been obtained, and the complex geometry relating the radar plat- form to the scene has been determined, the imaging model is used to calculate the power reflected from the ground back to the radar for each pixel in the image. The return power from a single resolution cell is given by the radar equation 242 O0 = P:G‘X\‘0 A (1) (4x)* R^ where the average transmitted power is represented by P, the two way gain of the transmitting/receiving antenna is given by G?, and the transmitted wavelength is given by A; the re- flectivity model, which is a function of wavelength and local incidence angle, among others, is o°; the area of the resolu- tion cell on the ground being sensed is A; and the distance from the antenna to the resolution cell being sensed is R. Speckle statistics, depending on the number of independent looks, are considered at this level: PPS Pr = (=) (Y (2) where Pr is the expected value of the return power from a resolution cell, Ÿ is a random number with a standard chi- square distribution having 2N degrees of freedom, and N is the number of independent looks. When the number of independent samples being averaged is large, (2) becomes =Pr(1+ 5) (3) where z is a Gaussian random variable with zero mean and unit variance. This return power calculated for each resolu- tion cell is coded into one pixel in the simulated image using the gray tone equation: n—1 2 Gr = Gre + (718 Pr +y1g M 1g K —lg[c)" Kc]) (4) where 2"^! denotes the number of possible gray values, x is the base 10 logarithm of the dynamic range of the radar signal being mapped into the linear range portion of the film TI dynamic range, and M is the transfer function of the radar re- ceiver; K is a constant depending upon the exposure time and the film processing and development, and y is a positive con- stant representing the slope of the linear portion of the film curve of density versus logarithm of exposure; Ic, Kc, Gre are calibration parameters. The graytone equation (4) represents the conversion of the signal returned from each resolution element into the appro- priate gray value for each image pixel after the elevation pro- file, dielectric categories, and spatial relationships of the var- ious cells have been properly considered. Multiple looks are also taken into account. The approach to simulation of SAR image products described by [Rainey, 1988] departs from most other simulation algo- rithms in the method of speckle generation. Speckle is pre- pared corresponding to the frequency, weightings and look av- eraging strategy of the radar-processor combination desired, and then multiplied by the source scene data preconditioned by the desired resolution. The method allows output pixel spacings to be specified independent of more fundamental system parameters. This accounts for the fact that, when dealing with SAR, pixel and resolution are two quite different concepts and quantities. Fundamental SAR spatial behavior occurs at the resolution cell level, whereas digital image rep- resentation is at pixel level. So, the authors argue that it is not sufficient to simulate speckle simply by imposing a ran- dom distribution on each pixel, and treating adjacent pixels as statistically independent. Their approach is as follows: 1. Image File (a) From a source file of ideal imagery, the reflectivity map, create one unspeckled image by convolving the source against the (desired) two-dimensional impulse response function. (b) If additive noise is to be included, add a constant to the resulting intermediate image. 2. Speckle File (a) Prepare N files each of which is a complex Gaus- sian pseudo-random field, essentially a "white noise’ source. Adjacent samples should be sta- tistically independent. (b) Bandpass filter each file with the two-dimensional frequency spectra corresponding to the radar and processor to be simulated. Each filter should be weighted and overlapped as per the described system. (c) Square law detect the filter outputs, and sum, again using any weighting representative of the system. Normalize. (d) Store the resulting real variates as a "speckle file’. This file, of course, is also in two dimensions. 3. Simulation (a) Subsample the image file and the speckle file to match the desired pixel spacing. (b) Pixel by pixel, multiply the two files together to create the final speckled image files. For a given radar-processor combination the computationally expensive creation of the speckle file is computed only once. International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B2. Vienna 1996

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