Reduction of Speckle Noise in SAR Images Using Hybrid Combination of Bootstrap Filtering and DWT

Author(s):  
Bassel Marhaba ◽  
Mourad Zribi
2018 ◽  
Vol 10 (8) ◽  
pp. 1295 ◽  
Author(s):  
Huifu Zhuang ◽  
Hongdong Fan ◽  
Kazhong Deng ◽  
Guobiao Yao

The neighborhood-based method was proposed and widely used in the change detection of synthetic aperture radar (SAR) images because the neighborhood information of SAR images is effective to reduce the negative effect of speckle noise. Nevertheless, for the neighborhood-based method, it is unreasonable to use a fixed window size for the entire image because the optimal window size of different pixels in an image is different. Hence, if you let the neighborhood-based method use a large window to significantly suppress noise, it cannot preserve the detail information such as the edge of a changed area. To overcome this drawback, we propose a spatial-temporal adaptive neighborhood-based ratio (STANR) approach for change detection in SAR images. STANR employs heterogeneity to adaptively select the spatial homogeneity neighborhood and uses the temporal adaptive strategy to determine multi-temporal neighborhood windows. Experimental results on two data sets show that STANR can both suppress the negative influence of noise and preserve edge details, and can obtain a better difference image than other state-of-the-art methods.


Author(s):  
Mr. Kaustubh Patil

The image taken by a satellite can be enhanced in terms of its resolution based on the interpolation can be obtained by DWT. Using DWT, the image at the input is divided into several sub bands and the speckle noise is also removed. Thereafter, the high-level images and low-level image at the input can be combined, to produce a better image applying IDWT. An intermediate stage for approximating high level is proposed here. The variation in detection approaches for SAR images are done by using image fusion strategy and novel fuzzy clustering algorithm. To retrieve an enhanced image, wavelet fusion directives are considered to combine the wavelet coefficients. A fuzzy C-means algorithm is proposed for identifying the altered and unaltered regions in the combined difference image.


Author(s):  
Khwairakpam Amitab ◽  
Debdatta Kandar ◽  
Arnab K. Maji

Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists of an input layer, several hidden layers, and an output layer. We have simulated MLP with two hidden layer in Matlab. Speckle noises were added to the target SAR image and applied MLP for speckle noise reduction. It is found that speckle noise in SAR images can be reduced by using MLP. We have considered Log-sigmoid, Tan-Sigmoid and Linear Transfer Function for the hidden layers. The MLP network are trained using Gradient descent with momentum back propagation, Resilient back propagation and Levenberg-Marquardt back propagation and comparatively evaluated the performance.


2019 ◽  
Vol 11 (9) ◽  
pp. 1041 ◽  
Author(s):  
Iman Heidarpour Shahrezaei ◽  
Hyun-cheol Kim

Due to the inherent characteristics of the electromagnetic wave scattering phenomenon, synthetic aperture radar (SAR) images are directly degraded by high-frequency multiplicative speckle (HMS) noise, which makes image de-speckling filter application and selection a challenge. In this regard, an adverse effects analysis of the HMS under implementation of seven different spatial de-speckling filters on a reference SAR image is considered in this paper. The investigation includes the formulation of the backscattered data and the HMS based on the pixel statistics and their distribution as an image noise behavioral analysis method. The resulting complex behavioral model is used for HMS power spectral density (PSD) function modeling. This paper also includes HMS system resolution effects analysis on the raw data generation (RDG) and the received frequency profile (RFP). An objective quality assessment procedure was also carried out to investigate both the de-speckled image resolution and the spatial filter evaluation in the presence of the HMS. As a result, the simulations verify that speckles are embedded within the high-frequency parts of the raw data, directly affecting the spatial resolution and the image resolution with non-specific patterns. The results also show that no spatial de-speckling filter consistently outperforms others, and their implementation is completely dependent on the texture, the system parameters, and their evaluation index. As a novel approach, HMS spectral behavioral modeling within the filtered images, as well as the proposed spatial de-speckling filter evaluation methods, are the proper techniques for optimum filter selection and specific purpose applications. The results are very helpful for remote sensing image restoration and data preservation when dealing with SAR images with a less fine resolution, such as ice-covered areas, coastal change detection, vegetation texture detection, geological structures mapping, and so forth. The SAR system resolution analysis is completed based on inversed problem solution (IPS) and with the help of a hybrid-domain image formation algorithm (IFA).


2020 ◽  
Vol 12 (3) ◽  
pp. 548 ◽  
Author(s):  
Xinzheng Zhang ◽  
Guo Liu ◽  
Ce Zhang ◽  
Peter M. Atkinson ◽  
Xiaoheng Tan ◽  
...  

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery.


2018 ◽  
Vol 215 ◽  
pp. 01002
Author(s):  
Yuhendra ◽  
Minarni

Image fusion is a useful tool for integrating low spatial resolution multispectral (MS) images with a high spatial resolution panchromatic (PAN) image, thus producing a high resolution multispectral image for better understanding of the observed earth surface. A main proposed the research were the effectiveness of different image fusion methods while filtering methods added to speckle suppression in synthetic aperture radar (SAR) images. The quality assessment of the filtering fused image implemented by statistical parameter namely mean, standard deviation, bias, universal index quality image (UIQI) and root mean squared error (RMSE). In order to test the robustness of the image quality, either speckle noise (Gamma map filter) is intentionally added to the fused image. When comparing and testing result, Gram Scmidth (GS) methods have shown better results for good colour reproduction, as compared with high pass filtering (HPF). And the other hands, GS, and wavelet intensity hue saturation (W-IHS) have shown the preserving good colour with original image for Landsat TM data.


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