Discontinuity adaptive SAR image despeckling using curvelet-based BM3D technique

2019 ◽  
Vol 17 (03) ◽  
pp. 1950016 ◽  
Author(s):  
D. Devapal ◽  
S. S. Kumar ◽  
R. Sethunadh
Keyword(s):  
Multiplicative Noise ◽  
Image Interpretation ◽  
Speckle Noise ◽  
Performance Measure ◽  
Block Matching ◽  
Manhattan Distance ◽  
Destructive Interference ◽  
Sar Image ◽  
Sar Images ◽  
Local Means

Synthetic Aperture Radar (SAR) is an all-weather, day and night satellite imaging technology where the radar is mounted on aircraft and successive pulses of radio waves are transmitted to illuminate the target scene. The signal processing of the recorded backscattered echoes produce SAR images. SAR images contain inherent multiplicative speckle noise which is formed due to the constructive and destructive interference of transmitted signals with the returning signals. Speckle noise appears as granular patterns and makes the image interpretation difficult. Non-local means approaches like Block Matching and 3D filtering (BM3D) are effective scheme for removing speckle noise from SAR images. This method gives good performance for additive noise but is not adaptive to curved edges and discontinuities that occur in SAR images affected by multiplicative noise. This paper proposes a three-step refined algorithm to adapt BM3D for despeckling multiplicative speckle noise. In the proposed scheme curvelet is used to find the transform coefficients and this modification in the transform domain improves the despeckling accuracy of BM3D. Also Wiener filtering is replaced with Importance Sampling Unscented Kalman Filtering (ISUKF) for better adapting to discontinuities in the real SAR image. An improved method of grouping is proposed here based on Manhattan distance which better adapts to constantly changing multiplicative noise statistics. A detailed comparative study is carried out on each step using various well-known performance measures. From the results, it is found that the proposed Curvelet-ISUKF-Manhattan BM3D (CIM-BM3D) method of despeckling has better values for all the performance measure and the results are also visually verified.

Based the Morphological Filtering BP Algorithm of SAR Image Recognition

2013 ◽  
Vol 760-762 ◽  
pp. 1486-1490
Author(s):  
Ding Ding Jiang ◽  
De Rong Cai ◽  
Qiang Wei
Keyword(s):  
Image Recognition ◽  
Learning Algorithm ◽  
Image Interpretation ◽  
Practical Significance ◽  
Sar Image ◽  
Detection Capability ◽  
Rotation Invariant ◽  
Sar Images ◽  
Diverse Background ◽  
Morphological Filtering

SAR image recognition is an important content of of aviation image interpretation work. In this paper, the characteristics of SAR images a practical significance of morphological filtering neural network model and its adaptive BP learning algorithm. As can be seen through the experimental results, the algorithm can not only adapt to the complex and diverse background environment, and has a displacement of the same continuous moving target detection capability, telescopic invariant and rotation invariant features.

scholarly journals Synthetic-Aperture Radar Image Despeckling based on Improved Non-Local Means and Non-Subsampled Shearlet Transform

2020 ◽  
Vol 49 (3) ◽  
pp. 299-307
Author(s):  
Zengguo Sun ◽  
Rui Shi ◽  
Wei Wei
Keyword(s):  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Sar Images ◽  
Shearlet Transform ◽  
Local Means ◽  
New Type ◽  
Non Local ◽  
Homogeneous Regions ◽  
Transform Domains

When Synthetic-Aperture (SAR) image is transformed into wavelet domain and other transform domains, most of the coefficients of the image are small or zero. This shows that SAR image is sparse. However, speckle can be seen in SAR images. The non-local means is a despeckling algorithm, but it cannot overcome the speckle in homogeneous regions and it blurs edge details of the image. In order to solve these problems, an improved non-local means is suggested. At the same time, in order to better suppress the speckle effectively in edge regions, the non-subsampled Shearlet transform (NSST) is applied. By combining NSST with the improved non-local means, a new type of despeckling algorithm is proposed. Results show that the proposed algorithm leads to a satisfying performance for SAR images.

scholarly journals Statistics Learning Network Based on the Quadratic Form for SAR Image Classification

2019 ◽  
Vol 11 (3) ◽  
pp. 282 ◽  
Author(s):  
Chu He ◽  
Bokun He ◽  
Xinlong Liu ◽  
Chenyao Kang ◽  
Mingsheng Liao
Keyword(s):  
Quadratic Form ◽  
Image Classification ◽  
Degrees Of Freedom ◽  
Image Interpretation ◽  
Sar Image ◽  
Statistical Features ◽  
Image Description ◽  
Linear Transformations ◽  
Sar Images ◽  
Learning Network

The convolutional neural network (CNN) has shown great potential in many fields; however, transferring this potential to synthetic aperture radar (SAR) image interpretation is still a challenging task. The coherent imaging mechanism causes the SAR signal to present strong fluctuations, and this randomness property calls for many degrees of freedom (DoFs) for the SAR image description. In this paper, a statistics learning network (SLN) based on the quadratic form is presented. The statistical features are expected to be fitted in the SLN for SAR image representation. (i) Relying on the quadratic form in linear algebra theory, a quadratic primitive is developed to comprehensively learn the elementary statistical features. This primitive is an extension to the convolutional primitive that involves both nonlinear and linear transformations and provides more flexibility in feature extraction. (ii) With the aid of this quadratic primitive, the SLN is proposed for the classification task. In the SLN, different types of statistics of SAR images are automatically extracted for representation. Experimental results on three datasets show that the SLN outperforms a standard CNN and traditional texture-based methods and has potential for SAR image classification.

Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image

2020 ◽  
pp. 489-510
Author(s):  
Khwairakpam Amitab ◽  
Debdatta Kandar ◽  
Arnab K. Maji
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Synthetic Aperture Radar ◽  
Back Propagation ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Multi Layer Perceptron ◽  
Sar Images ◽  
Artificial Neural

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.

scholarly journals Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

2020 ◽  
Vol 12 (3) ◽  
pp. 548 ◽  
Author(s):  
Xinzheng Zhang ◽  
Guo Liu ◽  
Ce Zhang ◽  
Peter M. Atkinson ◽  
Xiaoheng Tan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Speckle Noise ◽  
Sar Image ◽  
Phase Object ◽  
Sar Images ◽  
Two Phase ◽  
Object Based ◽  
Multi Temporal ◽  
Image Change Detection

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.

scholarly journals COLORIZING SENTINEL-1 SAR IMAGES USING A VARIATIONAL AUTOENCODER CONDITIONED ON SENTINEL-2 IMAGERY

2018 ◽  
Vol XLII-2 ◽  
pp. 1045-1051 ◽  
Author(s):  
M. Schmitt ◽  
L. H. Hughes ◽  
M. Körner ◽  
X. X. Zhu
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
Single Channel ◽  
Image Interpretation ◽  
Sar Image ◽  
Sar Images ◽  
Variational Autoencoder ◽  
Future Work ◽  
Special Case ◽  
Sentinel 2

In this paper, we have shown an approach for the automatic colorization of SAR backscatter images, which are usually provided in the form of single-channel gray-scale imagery. Using a deep generative model proposed for the purpose of photograph colorization and a Lab-space-based SAR-optical image fusion formulation, we are able to predict artificial color SAR images, which disclose much more information to the human interpreter than the original SAR data. Future work will aim at further adaption of the employed procedure to our special case of multi-sensor remote sensing imagery. Furthermore, we will investigate if the low-level representations learned intrinsically by the deep network can be used for SAR image interpretation in an end-to-end manner.

scholarly journals Speckle noise suppression method using multi-azimuth SAR images.

2021 ◽  
Vol 2083 (3) ◽  
pp. 032053
Author(s):  
Yingru Shi ◽  
Yang Liu ◽  
Peili Xi ◽  
Wei Yang ◽  
Hongcheng Zeng
Keyword(s):  
Noise Suppression ◽  
Speckle Noise ◽  
Image Sequences ◽  
Sar Images ◽  
Radar Images ◽  
Local Means ◽  
Structural Details ◽  
Novel Method ◽  
Non Local ◽  
Suppression Method

Abstract Synthetic aperture radar images play an important role in military and civilian fields, but the presence of speckle noise has an impact on subsequent tasks such as target detection and target interpretation. With the development of multi-azimuth observation mode, the obtained multi-azimuth image sequences have high similarities. Therefore, combined with multi-azimuth image sequences, a novel method of SAR image speckle noise suppression based on clustering is proposed in this paper. In this method, multi-azimuth joint filtering framework based on two-level filtering is proposed, in which pre-filtering for single image and joint filtering based on Non-local Means algorithm for multi-azimuth image are used to suppress the noise. And k-means clustering is used to optimize the search area in the multi-azimuth joint filtering, so as to effectively suppress speckle noise while retaining structural details.

scholarly journals Bankline detection of GF-3 SAR images based on shearlet

2021 ◽  
Vol 7 ◽  
pp. e611
Author(s):  
Zengguo Sun ◽  
Guodong Zhao ◽  
Marcin Woźniak ◽  
Rafał Scherer ◽  
Robertas Damaševičius
Keyword(s):  
Water Environment ◽  
Detection Algorithm ◽  
Morphological Processing ◽  
Map Matching ◽  
Sar Image ◽  
Sar Images ◽  
Multi Scale ◽  
Local Means ◽  
Active Imaging ◽  
Non Local

The GF-3 satellite is China’s first self-developed active imaging C-band multi-polarization synthetic aperture radar (SAR) satellite with complete intellectual property rights, which is widely used in various fields. Among them, the detection and recognition of banklines of GF-3 SAR image has very important application value for map matching, ship navigation, water environment monitoring and other fields. However, due to the coherent imaging mechanism, the GF-3 SAR image has obvious speckle, which affects the interpretation of the image seriously. Based on the excellent multi-scale, directionality and the optimal sparsity of the shearlet, a bankline detection algorithm based on shearlet is proposed. Firstly, we use non-local means filter to preprocess GF-3 SAR image, so as to reduce the interference of speckle on bankline detection. Secondly, shearlet is used to detect the bankline of the image. Finally, morphological processing is used to refine the bankline and further eliminate the false bankline caused by the speckle, so as to obtain the ideal bankline detection results. Experimental results show that the proposed method can effectively overcome the interference of speckle, and can detect the bankline information of GF-3 SAR image completely and smoothly.

scholarly journals Indoor Experiments of Bistatic/Multistatic GB-SAR with One-Stationary and One-Moving Antennae

2021 ◽  
Vol 13 (18) ◽  
pp. 3733
Author(s):  
Hoonyol Lee ◽  
Jihyun Moon
Keyword(s):  
Time Domain ◽  
Speckle Noise ◽  
The Other ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Sar Images ◽  
Airborne Sar ◽  
Transmitting Antenna ◽  
Azimuth Resolution ◽  
Ku Band

Ground-based synthetic aperture radar (GB-SAR) is a useful tool to simulate advanced SAR systems with its flexibility on RF system and SAR configuration. This paper reports an indoor experiment of bistatic/multistatic GB-SAR operated in Ku-band with two antennae: one antenna was stationary on the ground and the other was moving along a linear rail. Multiple bistatic GB-SAR images were taken with various stationary antenna positions, and then averaged to simulate a multistatic GB-SAR configuration composed of a moving Tx antenna along a rail and multiple stationary Rx antennae with various viewing angles. This configuration simulates the use of a spaceborne/airborne SAR system as a transmitting antenna and multiple ground-based stationary antennae as receiving antennae to obtain omni-directional scattering images. This SAR geometry with one-stationary and one-moving antennae configuration was analyzed and a time-domain SAR focusing algorithm was adjusted to this geometry. Being stationary for one antenna, the Doppler rate was analyzed to be half of the monostatic case, and the azimuth resolution was doubled. Image quality was enhanced by identifying and reducing azimuth ambiguity. By averaging multiple bistatic images from various stationary antenna positions, a multistatic GB-SAR image was achieved to have better image swath and reduced speckle noise.

SAR Image Denoising Using an Improved Adaptive Bitateral Filter

2013 ◽  
Vol 842 ◽  
pp. 672-677
Author(s):  
Hua Zhang Wang ◽  
Qin Zhen Huang
Keyword(s):  
Image Data ◽  
Bilateral Filter ◽  
Speckle Noise ◽  
Sar Image ◽  
Gaussian Filter ◽  
Sar Images ◽  
Operation Speed ◽  
Human Interpretation ◽  
Filtering Effect ◽  
The Mathematical Model

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise. The presence of speckle damages radiometric resolution, at the same time, it hampers the human interpretation and scene analysis for SAR images. On the base of studying and analyzing the mathematical model of the bilateral filter, the paper proposed a modified adaptive bilateral filter (MABF). First, it separates non-independent two-dimensional Gaussian filter into two independent one-dimensional Gaussian filter, which improves the operation speed greatly. Then through the effective noise parameter estimation, it adaptively selects optimal parameters, which improves the filtering effect. The real SAR image data is used to test the presented method and the experimental results verify that MABF is feasible and effective.

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