Despeckling Synthetic Aperture Radar Images with Cloud Computing using Graphics Processing Units

Transcription

1 Depeckling Synthetic Aperture Radar Image with Cloud Computing uing Graphic Proceing Unit Matej Keneman 1, Dušan Gleich, Amor Chowdhury 1 1 Margento R & D d.o.o., Gopovetka ceta 84, Maribor, Slovenia matej.keneman@gmail.com Faculty of Electrical Engineering and Computer Science, Univerity of Maribor, Slovenia Abtract Thi paper preent the implementation of Synthetic Aperture Radar (SAR) image enhancement and information extraction technique uing multicore Graphic Proceing Unit (GPU) connected into a cloud computing environment. The Bayeian approach to SAR image depeckling and information extraction i preented. The firt order Bayeian inference i ued to etimate a maximum a poteriori (MAP) etimate. A prior i modeled uing Gau-Markov Random Field (GMRF). The econd order Bayeian inference i ued to find the bet model parameter, which repreent texture information in SAR image. The algorithm i rewritten in matrix form to fully exploit GPU computing power. GMRF on GPU give good reult for depeckling and information extraction, but thi algorithm i alo very computationally demanding. Thi paper preent the implementation of the MAP etimator and evidence maximization uing GPU. The image i divided into ubblock and each ubblock ha a many thread a there are pixel inide the ubblock. The etimation of MAP olution and it correponding model i computed eparately in each thread in the ubblock. The GPU implementation of the model-baed depeckling i about 95-time fater a the implementation of the ame algorithm on multicore CPU on a development ytem. The whole cloud computing environment ha proven to be fat and robut. Keyword: Cloud Data Computing, Bayeian inference, SAR, peckle reduction, GPU. 1. Introduction Synthetic Aperture Radar (SAR) i an all-weather imaging ytem, whoe performance ha been increaing over the pat few year. Nowaday, atellite platform achieve reolution below 1 meter and the increae in image detail make SAR data very attractive. Unfortunately, SAR data are corrupted by peckle noie, which make cene interpretation very difficult. Therefore, image retoration technique have been developed over the pat few decade in order to remove peckle from SAR image and preerve all textural feature of SAR image. Many depeckling method have been developed and they all depeckle the intenity /10/$ IEEE or amplitude part of SAR image [1-6]. The aim of image retoration i to try to etimate the ideal true image from the noiy one. The Bayeian approach conit of interpreting probabilitie baed on a ytem of axiom that decribe incomplete information rather than randomne and allowing an inference proce, which for a given data et and hypothei pace, aign probabilitie to the hypothei. Cloud computing i a technology ued all over the world, where the tendency i to put all application and ervice on central remote erver to proce and maintain data, while the uer hould ue only the eential tool like web brower. Thu cloud computing allow conumer to ue application without intallation and acce to file from all over the world. Thi technology allow u much more efficient computing by centralizing proceing power, memory, torage and bandwidth. The gaming market ha demanded powerful graphic proceor for high-definition movie, 3D game, thu a highly multithreaded and parallel multicore programmable GPU were introduced. Thee GPU have incredible advantage over CPU in term of mathematical computation. The programmable part of the GPU conit of the two proceor, fragment and vertex. Nowaday GPU upport SIMD (Single Intruction Multiple Data) intruction with increaing IO data and number of proceor. Thi parallelim i more important in GPU programming than raw proceing power. The availability of high level language for programming GPU ha puhed boundarie even further [7]. There are drawback, which include totally different programming model. Much reearch i done in the field of numerical computation (PDE olving, matrix operation), phyical imulation, ray tracing, image proceing [4]. However, in thi article we tried to ae the feaibility of uing cloud computing baed on GPU for image denoiing in a much fater way than it i poible with conventional CPU. The goal of thi paper wa to rewrite GMRF model baed depeckling algorithm into matrix form, thu harneing the power of parallelization ued in GPU. By doing thi a much fater algorithm i obtained, which i crucial a a pot-proceing tep in ground-tation. Then a etup o cloud computing with GPU worktation 195

2 i propoed, which can be acceed from all over the globe.. Speckle reduction algorithm Coherent radar beam interact with each other, either contructively or detructively. That caue bright or dark pixel in SAR image named peckle noie. Thu peckle noie i a well-known phenomenon in radar remote ening ytem, even though it can appear at any type of coherent radiation. Fig. 1 depict a mall crop from much larger TerraSAR-X image acquired on 6 th June 009 in dual polarization mode. The image i taken near hydro plant Zlatolije that i in the vicinity of the city Maribor, Slovenia. Figure 1. SAR image containing peckle noie captured in the vicinity of Maribor, Slovenia with a TerraSAR-X atellite Baye approach to peckle noie reduction i ued for image quality enhancement and image retoration, a well a for variou technique for information extraction. A firt order Baye inference i ued for maximum a poteriori (MAP) etimation. Prior in Baye formula i modeled with Gau-Markov random field (GMRF). In order to find the bet model parameter a econd order Baye inference i ued. Thi method ha proven to be good at peckle noie removal and texture etimation [1-3]. Baye inference i given by p y x p x y,, px py (1) where y i image with a peckle noie, x i a noie-free image, are model parameter, p(y x,) repreent likelihood, p(x ) i prior and p(y ) i evidence. Becaue evidence doe not play any role in maximization proce over x, it can be neglected from further model derivation. Thi can be written by Eq. (). xˆ y arg max p y x, p x () xˆ Speckle noie in the original SAR image i modeled a multiplicative noie y = xn, where n repreent noie. Probability denity function (pdf) of likelihood i modeled by gamma ditribution. p y x L1 y L y exp L x xl x (3) where L i equivalent number of look defined a quared mean value divided by quared tandard deviation (L = mean /td ), i current pixel location and i the Gamma function. Gau-Markov random field belong to a Gibb family of model [8], which are uitable for decribing random procee a well a SAR image [1-3]. GMRF are given by 1 x r x r N r xr py exp (4) where N S i pixel neighborhood, r define neighboring pixel around a central pixel, and r are texture parameter. MAP etimator for Baye theorem i written by Eq. (). Thu by combining thee two equation together the MAP for Gau-Markov random field i given by Eq. (5), where the MAP olution of the GMRF i calculated analytically. x 4 3 x r xr xr L x L y 0 (5) rn The texture parameter r for the GMRF model define it model parameter. Thoe parameter are etimated uing evidence maximization procedure. The evidence py py xpx dx cannot be computed analytically; therefore the approximation i made by uing the Heian approximation. A logarithmic form can be ued to implify the equation: NN 1 log p y log log hii i1 log py ˆ log ˆ i xi p xi where h ii i given by h ii (6) 6Ly L 1 1 j (7) xmap xmap jn 3. Implementation on GPU Model baed depeckling (MBD), which ue Gau- Markov random field for image denoiing and 196

3 information extraction wa parallelized to take the advantage of the immene power packed in graphic proceing unit (GPU). The peckled image (i.e. input image) i divided into grid of block with known dimenion N N. Thee dimenion are conitent with block ize on GPU. In each of thee thread block a known number of thread were introduced and each of thee thread wa computed imultaneouly over more treaming proceor. The algorithm i organized in a way that the firt MAP etimation i done by taking one grid block of picture, where etimation take place on multiproceor and their execution i timehared. For data acquiition a texture fetching from texture memory wa ued. All thread of thi block executing on ingle multiproceor divide it reource equally amongt themelve. Thi block of image i executed many time, becaue the MAP etimation algorithm for determining texture parameter ha to reach convergence. In order to preerve edge and region border the region growing algorithm and adaptive neighborhood were ued for each ubblock. At the end of proceing each ubblock a coefficient of variation [1] i computed, which i ued to eparate homogeneou and heterogeneou area. 4. Cloud computing with GPU mapping, etc.). Fig. i a graphical repreentation of cloud computing mainframe ued in our cae. The idea behind thi cloud computing environment i that thee TerraSAR-X image are very large in ize, and ometime they exceed GB of dik ize, even though they are delivered in a compreed archive. Thu it i a neceity to have at leat one external hard drive to carry around thoe image. Intead of thi an idea wa to take all image online that could be acceed from around the globe with a imple election of deired region on the globe (i.e., if that image i available in the databae). For the purpoe of electing available image for a given region on the Earth, a Google Map ervice [9] wa ued. Data erver check every product file (i.e., meta-information about the image capture) for any new image and convert it coordinate into geo-referenced coordinate that can be diplayed a a rectangle on the Google Map ervice. Fig. 3 i an example of approximate region in the vicinity of Maribor, Slovenia. Graphic erver NAS Data erver Figure. Propoed cloud computing conceptual deign for SAR image ditribution The next tep repreent the etup of a cloud computing mainframe. Cloud computing i Internetbaed proceing, whereby hared reource are available to uer over the Internet. Our cloud computing environment i meant to ditribute the TerraSAR-X image or portion of it to legitimate uer over the web-baed application. Therefore the cloud computing environment i a maller one, compoed of Data erver that indexe image available on network torage device (NAS) connected to the ame ubnet, and Graphic erver that i ued for on-the-fly proceing of uer requet. The need of ditributing depeckled image ha arien, becaue they are more natural-like and can be directly compared to optical atellite image ditributed over the Google Earth [9] mainframe (e.g., for lecture or repreentation, land Figure 3. Approximate region where it i at leat one available image in the databae Table 1. All available TerraSAR-X image for current region election viible in Fig. 3 (GPS coordinate are for the firt image pixel) Date UTC : : : : : : :09 GPS coordinate N E N E N E N E N E N E N E Incidence angle In thi cae of electing the rectangle hown in Fig. 3 even different TerraSAR-X image are available and are gathered in Table 1. All image alo have a Google Earth *.kml file, which contain detailed information about the capture ite. The conumer can imply click on thi link, and a file i tranferred from the data 3 197

4 erver, where the file i opened inide Google Earth application. Fig. 4 depict an example for the TarraSAR-X acquiition ite on 4 th September 009. ize, if there are polarization, which polarization i of main interet for the uer. Finally, the MBD algorithm i applied to the elected region by uing a predefined parameter for window ize and model order i et to [1]. MBD output image i in 3-bit floating point repreentation and aved on the Graphic erver, meanwhile the image i converted to 8-bit gray-cale image that i then tranferred to the uer. Finally, the Graphic erver repond to Data erver that the requet ha been uccefully accomplihed. Figure 4: Detailed acquiition information about the available TerraSAR-X image found in databae Finally the uer can elect a deired rectangle of all available image, where image decription i hown a table on the page. Thi table contain information about the type of image, polarization, capturing date and time, ground and range reolution, frequency, etc. After the uer decide which TerraSAR-X image i intereting for him at the moment, a quick-look image i tranferred from NAS dik array to web brower (Fig. 5). Let aume the uer elect an image from 6th June 009, which i alo depicted in Fig. 5. Now it i poible to elect which region i of mot interet, which i done by drawing a rectangle by moue dragging over the image or by manually inputting value of the image crop (Fig. 6). Thi ervice run on ap programming language. Figure 5. TerraSAR-X image of the area of interet taken on 6th June 009 On the Fig. 6 the uer elect an area, where i a bridge over the river Drava and elect the image of a ize At thi time the depeckled image crop i ent to the uer, meanwhile requet for image denoiing i end to the Graphic erver. Graphic erver only get baic intruction, uch a: where i the image on the NAS dik array, tarting pixel in an image and the crop Figure 6. Uer elect a deired area, which repreent a crop election of Fig Experimental reult A the reolution grow, the peckle effect i more intene, thu a ubampling ha to be done or a depeckling algorithm ha to be applied in order to ee what i on that image. The GMRF model baed depeckling algorithm wa ued in thi tudy for repreentation proce only. Thu an algorithm performance wa aeed a well a overall performance. The overall algorithm wa teted on TerraSAR-X image taken on June 6, 010, where an image cut with pixel wa obtained and ubample by a factor of. The input image i thu hown in Fig. 1. The detail of thi performance are preented in the ubection 4.1. Next a etup for web performance tet wa et, where a client PC wa a low-end computer running Window XP and a Chrome web brower. Performance tet i decribed in detail in ubection GPU algorithm performance A ynthetic image of ize that i corrupted by L=3 peckle noie and wa depeckled uing the CPU and GPU approach, becaue we are intereted in a peed-up gained by GPU proceing power. For the algorithm development and teting a econdary PC wa ued, which included different hardware. For GPU proceing an Nvidia 9600 GT graphic card wa ued, which ha 8 multiproceor and each proceor ha 8 core, and the warp ize equal 3 thread. Moreover, on the hot ide an Intel Quad core Q9450 with 4 GB of DDR 800 MHz wa ued in combination with 64-bit Window environment. Table repreent average execution time for CPU and GPU implementation for the MBD method applied 4 198

5 on the ynthetic image (i.e. the algorithm wa run 10 time in order to get reliable averaging value). Thi algorithm i divided into MAP etimation (MAP), computation of evidence, evidence maximization (EVM), region growing (Reg. Grow.) and computation of coefficient of variation (CV). A the experimental reult how, the improvement over execution time between GPU and CPU i ignificant (about 95-time). However, the MSE between original and recontructed image for the MBD on CPU and GPU i and 196, repectively. Fig. 7 depict viual change between ynthetically generated SAR image that were deliberately corrupted by a peckle noie with equivalent number of look equal to 3. Fig. 8 depict an example of denoied TerraSAR-X image (Fig. 1), which i an output from the MBD algorithm applied on the graphic proceing unit. ize (thread block), maller window ize 1 1 (each thread), model order i equal to. Table : Execution time of CPU and GPU baed on the MBD algorithm (all execution time are in econd) CPU GPU MAP Evidence EVM Reg. Grow CV Total Figure 7. CPU and GPU comparion uing a ynthetic image corrupted with L=3 peckle noie Figure 8. Depeckled TerraSAR-X image uing MBD algorithm applied on a GPU Fig. 9 repreent a comparion between MBD depeckling carried on a CPU (left) and a GPU (right). Thoe two image are alo cut out from the previouly elected TerraSAR-X image taken on 6 th June 009. The MBD parameter were in both cae: larger window Figure 9: Comparion of image output generated by MBD algorithm applied on CPU (left) and GPU (right) 4.. Web-baed performance Setup for web-baed cloud computing environment contain a Graphic erver, Data erver, and NAS dik array containing all SAR image. Graphic erver contain two nvidia Fermi GTX480 graphic card connected into the SLI mode. Each card ha 480 CUDA core operating at 1401 MHz, while running it GDDR5 memory at 3696 MHz through 384-bit memory bu. Thi enable roughly GB/ of memory bandwidth per graphic card. Graphic erver containing thee two card wa equipped with Intel Core i7 860 proceor and 8 GB of ytem memory operating at 64-bit. Data erver run ap ervice for uer web-baed login, and indexe newly added image on the NAS array. Data erver hardware pecification are: Intel Quad core Q9450 with four GB of DDR 800 MHz, while the operating ytem run on 64-bit Window environment. NAS dik array contain GB 700 rpm dik connected into RAID 0 array. All component are connected through internal Linky SRW008MP witch at a connection peed of 1.0 Gbit/

6 In order to tet Graphic erver denoiing peed a erie of tet were carried out for variou crop ize (i.e., 56 56, 51 51, , and ). The reult are gathered in Table 3. The whole capturing time (i.e., the time when the uer actually get a denoied image) extend by the bandwidth of your Internet connection. However, our meaurement have hown a light increae in delivery time on the ame ubnet, which wa on average 0.1 econd. Table 3: Graphic erver performance review (all given value are in econd) Mean value Standard deviation Dicuion By comparing the actual algorithm performance between GPU and CPU proceing time, change i immene. Even with relatively low-cot graphic proceing unit available on the market for quite a while now, it poible to achieve a peed-up of 95-time. That i a good programming invetment by writing a parallel MDB model, becaue in the area of image proceing every operation on SAR image nearly divide the execution time by 95, and the uer i able to manipulate more image in le time. However, there i a drawback that ha hown during GPU and CPU comparion, which i a little higher value of mean quare error. The light increae in MSE value between the original and recontructed image i a conequence of rounding up and uing fater function verion. By the time thi algorithm wa developed only 3-bit floating point calculation wa available for computation on GPU. The real world teting uing Graphic erver i even fater than conventional CPU. With thi hardware we were able to proce image crop within a half econd, which i a reaonable lag for a web-browing conumer. By looking to Table 3 reaonable table and contant proceing time can be oberved. However, a major drawback i oberved in multi-uer cenario, becaue GPU algorithm implementation i not intended for time-haring, thu a queuing i introduced. 6. Concluion In thi paper we preented the ability to depeckle SAR image uing cloud computing baed on GPU. Thi environment i devided into Graphic erver for SAR image depeckling, Data erver for running ap web ervice and indexing of SAR image, and NAS dik array that tore all available SAR image. Model baed depeckling algorithm wa ued for the purpoe of image recontruction, becaue of the ability to convert it equation into matrix form, thu exploiting all available thread on GPU. Intead of carrying around all SAR image tored on external dik, thoe image are available within a few moue click. Even if the uer ha the TerraSAR-X image, it cannot be viewed by any conventional image program, becaue of the file tructure, and thu needing the pecial (in mot cae expenive) oftware. The overall latency for denoied SAR image retrieval under a econd from every peronal computer connected to the Internet i a remarkable reult. All it take for conumer i a web brower no matter what operating ytem he ue. Future work i intended to expand cloud computing environment to be able to imultaneouly proce more uer requet. With every new generation of graphic card capable of CUDA computing they are equipped with more onboard memory and fater proceor, thu thi algorithm will be calculated even in le time. Moreover, the new generation i alo able to compute in double preciion floating-point, which can improve the difference of MSE between CPU and GPU. An expanion of thi algorithm i to implement oil moiture etimation algorithm, where a uer can elect an area from TerraSAR-X image and the Graphic erver will calculate emi-empirical model ued for volumetric oil moiture etimation and thu artificially color output image regarding the water content. Thi will be a good indicator for biologit exploring the nature in the outermot region of our planet Earth. Reference [1] Walea M., and Datcu M., Model-baed depeckling and information extraction from SAR image, IEEE Tran. Geoci. Remote Sen., Sep. 000, (38), [] Hebar M., Gleich D., and Cucej Z., Auto-binomial model for SAR image depeckling and information extraction, IEEE Tran. Geoci. Remote Sen., 009, (47), [3] Gleich D., Keneman M., and Datcu M., Depeckling of terrasar-x data uing econd-generation wavelet, IEEE geoci. remote en. lett., 010, 7(1), [4] D. Gleich and M. Datcu, Gau-markov model for ar image depeckling, IEEE Signal Proce. Lett., vol. 13, no. 6, pp , June 006. [5] F. Argenti, T. Bianchi, and L. Alparone, Multireolution map depeckling of ar image baed on locally adaptive generalized gauian pdf modeling, IEEE Tran. Image Proce., vol. 15, no., pp , Nov [6] D. O. Duda, P. E. Hart, and D. Stork, Pattern Claification. Reading, MA: John Wiley and Son, 197. [7] GPU computing application developed on the CUDA architecture, [8] R. Chelappa and A. Jain, Markov Random Field: Theory and Application, Academic Pre, London, UK, 1993, ch. 6. [9] Google Earth,