ارائه یک روش کاهش اسپکل روی تصاویر رادار با روزنه مصنوعی

وجود اسپکل در تصاویر رادار امری اجتناب ناپذیر است. نویز اسپکل یک آشفتگی نقطه ای است که معمولا به عنوان نویز ضرب شونده در تصاویر تک پلاریزه مدل می شود. این نویز که وابسته به سیگنال است به دلیل نوسانات فاز سیگنال های بازگشتی امواج الکترومغناطیسی بوجود می آید که به صورت نقطه نقطه ظاهر می شود. حضور اسپکل، تفسیر و آنالیز تصویر را پیچیده تر می کند و باعث کاهش دسترسی به اطلاعات تصویر می شود لذا انتخاب الگوریتم مناسب کاهش نویز اسپکل حائز اهمیت است. در مقاله حاضر ابتدا به توضیح فیلترهای متداول کاهش اسپکل مانند: میانگین، کوان، فراست، لی، لی بهبودیافته و گامامپ پرداخته شده، سپس بر طبق ویژگی های آماری اسپکل و ویژگی های بافتی تصویر sar یک الگوریتم تطبیقی برای کاهش نویز اسپکل با پنجره تغییرپذیر از لحاظ بعد بر پایه انحراف معیار نسبی پیشنهاد شده است. برای بررسی و ارزیابی الگوریتم پیشنهادی و سایر فیلترهای متداول، از یک تصویر شبیه سازی شده چهاردید sar و دو تصویر واقعی، اخذ شده از دو سنجده هوابرد مختلف، استفاده شد. نتایج تجربی نشان دهده عملکرد رضایت بخش الگوریتم پیشنهادی در حذف اسپکل، همراه با حفظ بسیار خوب ویژگی های لبه، اهداف نقطه ای و میانگین تصویر نسبت به سایر فیلترها می باشد. به عنوان مثال شاخص های enl و snr برای برای ناحیه 1 از تصویر اول به ترتیب 17.22 و 7.91 بود که بالاتر از مقدار سایر فیلترهای متداول بود.

Proposal speckle reduction algorithm for SAR images

The existence of speckle in Radar images is an inevitable occurrence. The speckle noise is a granular disturbance that models often as a multiplicative noise in singlechannel SAR images. This noise that dependent on signal, introduces for variations of phase of returned signals that appears as pointpoint pattern. Being speckle, make complex more the explanation and analysis of images and also decrease the access to the image data, so it is important which appropriate specklereduction algorithm should be choosing. therfore, a section is allocated to the introduction of speckle noise model. It provides some important facts about how the speckle formed and explains which probably density function is followed by the amplitude and the intensity image. Several adaptive filtering methods have been discussed to deal with issue in this paper such as: Mean, Kuan, Frost, Lee, enhanced Lee and GammaMAP. Then according to statistic characteristics of speckles and texture characteristics of SAR images an adaptive specklereduction algorithm with sizechangeable window based on relative standard deviation have been put forward. This new algorithm uses a moving window like other typical filters, but its window is divided to four smaller windows that every of them is called subwindow. The relative standard deviation of every subwindow is used as compare factor. If all subwindows are in homogenous region the mean filter is applied on whole initial window. And if any subwindow has the edge or highfrequency information, it must be omitted from proceeding process. The proposed filter is sizechangeable because, if all subwindows are not accepted by the rules, the filter need to reduce the size of initial window to provide a region for filtering process.This paper benefits some worthy indices to demonstrate the ability of proposed filter against common filters such as equivalent number of looks (ENL), speckle suppression and mean preservation index (SMPI), edge save index (ESI), mean square error (MSE) and signal to noise ratio (SNR). To analyze the proposal algorithm and other common filters, used a simulated fourlook SAR image and two real SAR images i.e. Flevoland dataset from AirSAR airborne SAR sensor and Oberpfaffenhofen dataset from ESAR airborne SAR sensor, respectively. The simulated SAR image is determined according to multiplicative model and gamma distribution. It is used to show a primary evaluation of proposed filter and other filters. In the first step, the results are satisfying. For example, the indices of standard deviation (SD) and ELN for the proposed filter are 39.53 and 192.59 that in comparison to other filters are gratifying and agreeable. In the next step, the all filters that are described in this paper are applied on Flevoland dataset. As the experience results show, the proposal algorithm has a satisfying performance in removing speckle noise along with very good saving edge characteristics, targets scene and mean of image toward usual specklereduction filters. For instance, the indices of ENL and SNR for area 1 of first image are 17.22 and 7.91, respectively, that are highest values between other common filters. To survey more precisely, the mean and the enhanced Lee filters are selected to compare to the proposed filter by second real dataset in the ways of ability to remove speckle, preserve edge and maintain point target.