scholarly journals Resolutional Analysis of Multiplicative High-Frequency Speckle Noise Based on SAR Spatial De-Speckling Filter Implementation and Selection

2019 ◽  
Vol 11 (9) ◽  
pp. 1041 ◽  
Author(s):  
Iman Heidarpour Shahrezaei ◽  
Hyun-cheol Kim
Keyword(s):  
High Frequency ◽  
Speckle Noise ◽  
Behavioral Model ◽  
Spatial Filter ◽  
Image Resolution ◽  
Assessment Procedure ◽  
Problem Solution ◽  
Data Generation ◽  
Sar Images ◽  
Raw Data

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).

scholarly journals Multiscale and Multitemporal Road Detection from High Resolution SAR Images Using Attention Mechanism

2021 ◽  
Vol 13 (16) ◽  
pp. 3149
Author(s):  
Xiaochen Wei ◽  
Xikai Fu ◽  
Ye Yun ◽  
Xiaolei Lv
Keyword(s):  
Contextual Information ◽  
Temporal Characteristic ◽  
Semantic Segmentation ◽  
Speckle Noise ◽  
Attention Mechanism ◽  
Image Resolution ◽  
Temporal Consistency ◽  
Road Detection ◽  
Sar Images ◽  
Sar Imagery

Road detection from images has emerged as an important way to obtain road information, thereby gaining much attention in recent years. However, most existing methods only focus on extracting road information from single temporal intensity images, which may cause a decrease in image resolution due to the use of spatial filter methods to avoid coherent speckle noises. Some newly developed methods take into account the multi-temporal information in the preprocessing stage to filter the coherent speckle noise in the SAR imagery. They ignore the temporal characteristic of road objects such as the temporal consistency for the road objects in the multitemporal SAR images that cover the same area and are taken at adjacent times, causing the limitation in detection performance. In this paper, we propose a multiscale and multitemporal network (MSMTHRNet) for road detection from SAR imagery, which contains the temporal consistency enhancement module (TCEM) and multiscale fusion module (MSFM) that are based on attention mechanism. In particular, we propose the TCEM to make full use of multitemporal information, which contains temporal attention submodule that applies attention mechanism to capture temporal contextual information. We enforce temporal consistency constraint by the TCEM to obtain the enhanced feature representations of SAR imagery that help to distinguish the real roads. Since the width of roads are various, incorporating multiscale features is a promising way to improve the results of road detection. We propose the MSFM that applies learned weights to combine predictions of different scale features. Since there is no public dataset, we build a multitemporal road detection dataset to evaluate our methods. State-of-the-art semantic segmentation network HRNetV2 is used as a baseline method to compare with MSHRNet that only has MSFM and the MSMTHRNet. The MSHRNet(TAF) whose input is the SAR image after the temporal filter is adopted to compare with our proposed MSMTHRNet. On our test dataset, MSHRNet and MSMTHRNet improve over the HRNetV2 by 2.1% and 14.19%, respectively, in the IoU metric and by 3.25% and 17.08%, respectively, in the APLS metric. MSMTHRNet improves over the MSMTHRNet(TAF) by 8.23% and 8.81% in the IoU metric and APLS metric, respectively.

SAR Image Denoising Algorithm Based on Bayes Wavelet Shrinkage and Fast Guided Filter

2019 ◽  
Vol 23 (1) ◽  
pp. 107-113
Author(s):  
Xiu Jie Yang ◽  
Ping Chen ◽  
◽  
Keyword(s):  
Frequency Domain ◽  
High Frequency ◽  
Low Frequency ◽  
Speckle Noise ◽  
Guided Filter ◽  
Wavelet Domain ◽  
Wavelet Shrinkage ◽  
Sar Images ◽  
Edge Information ◽  
Posteriori Estimation

To remove the speckle noise of synthetic aperture radar (SAR) images, a novel denoising algorithm based on Bayes wavelet shrinkage and a fast guided filter is proposed. According to the statistical properties of SAR images, the noise-free signal and speckle noise in the wavelet domain are modeled as Laplace and Fisher-Tippett distributions respectively. Then a new wavelet shrinkage algorithm is obtained by adopting the Bayes maximum a posteriori estimation. Speckle noise in the high-frequency domain of SAR images is shrunk by this new wavelet shrinkage algorithm. As the wavelet coefficients of the low-frequency domain also contain some speckle noise, speckle noise in the low-frequency domain can be further filtered by the fast guided filter. The result of the denoising experiments of simulated SAR images and real SAR images demonstrate that the proposed algorithm has the ability to better denoise and preserve edge information.

Despeckling of Sar Images Using Shrinkage of Two-Dimensional Discrete Orthonormal S-Transform

2020 ◽  
pp. 2150023
Author(s):  
Priya R. Kamath ◽  
Kedarnath Senapati ◽  
P. Jidesh
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Relevant Information ◽  
Detection Algorithm ◽  
Two Dimensional ◽  
Sar Images ◽  
S Transform ◽  
Processing Step ◽  
Frequency Components ◽  
Shock Filter

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.

scholarly journals A Spatial-Temporal Adaptive Neighborhood-Based Ratio Approach for Change Detection in SAR Images

2018 ◽  
Vol 10 (8) ◽  
pp. 1295 ◽  
Author(s):  
Huifu Zhuang ◽  
Hongdong Fan ◽  
Kazhong Deng ◽  
Guobiao Yao
Keyword(s):  
Change Detection ◽  
Window Size ◽  
Speckle Noise ◽  
Adaptive Strategy ◽  
Negative Influence ◽  
Sar Images ◽  
Spatial Homogeneity ◽  
Difference Image ◽  
Negative Effect ◽  
Adaptive Neighborhood

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.

TOPS Mode Raw Data Generation From Wide-Beam SAR Imaging Modes

2012 ◽  
Vol 9 (4) ◽  
pp. 720-724 ◽  
Author(s):  
Wei Xu ◽  
Yunkai Deng ◽  
Fan Feng ◽  
Yue Liu ◽  
Guangting Li
Keyword(s):  
Data Generation ◽  
Raw Data ◽  
Wide Beam ◽  
Sar Imaging

scholarly journals Optimization of Classified Satellite Images using DWT and Fuzzy Logic

2013 ◽  
Vol 2 (02) ◽  
pp. 08-12
Author(s):  
Mr. Kaustubh Patil
Keyword(s):  
Satellite Images ◽  
Clustering Algorithm ◽  
Speckle Noise ◽  
Intermediate Stage ◽  
Sar Images ◽  
Fusion Strategy ◽  
Difference Image ◽  
Fuzzy Clustering Algorithm ◽  
High Level ◽  
Fuzzy C Means Algorithm

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.

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