Segmentation of SAR Image using Fuzzy C-Means and Filters

2020 ◽  
Vol 8 (1) ◽  
pp. 84-90
Author(s):  
R. Lalchhanhima ◽  
◽  
Debdatta Kandar ◽  
R. Chawngsangpuii ◽  
Vanlalmuansangi Khenglawt ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
State Of The Art ◽  
Speckle Noise ◽  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Spatial Filters ◽  
Fuzzy C Means ◽  
Automatic Clustering ◽  
Intensity Information

Fuzzy C-Means is an unsupervised clustering algorithm for the automatic clustering of data. Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore the segmentation process can not directly rely on the intensity information alone but must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use the fuzzy nature of classification for the purpose of unsupervised region segmentation in which FCM is employed. Different features are obtained by filtering of the image by using different spatial filters and are selected for segmentation criteria. The segmentation performance is determined by the accuracy compared with a different state of the art techniques proposed recently.

Synthetic aperture radar image segmentation using supervised artificial neural network

2020 ◽  
Vol 16 (4) ◽  
pp. 397-408
Author(s):  
R. Lalchhanhima ◽  
Goutam Saha ◽  
Morrel V.L. Nunsanga ◽  
Debdatta Kandar
Keyword(s):  
Image Segmentation ◽  
Synthetic Aperture Radar ◽  
Ground Truth ◽  
Speckle Noise ◽  
Synthetic Aperture Radar Image ◽  
Radar Image ◽  
Synthetic Aperture ◽  
Intensity Information ◽  
Ground Truth Image ◽  
Aperture Radar

Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore, the segmentation process can not directly rely on the intensity information alone, but it must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use supervised information about regions for segmentation criteria in which ANN is employed to give training on the basis of known ground truth image derived. Three different features are employed for segmentation, first feature is the original image, second feature is the roughness information and the third feature is the filtered image. The segmentation accuracy is measured against the Difficulty of Segmentation (DoS) and Cross Region Fitting (CRF) methods. The performance of our algorithm has been compared with other proposed methods employing the same set of data.

scholarly journals SAR Target Configuration Recognition via Product Sparse Representation

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3535
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing
Keyword(s):  
Sparse Representation ◽  
State Of The Art ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Product Model ◽  
Sar Images ◽  
Stationary Target ◽  
Target Configuration ◽  
Sparse Vector

Sparse representation (SR) has been verified to be an effective tool for pattern recognition. Considering the multiplicative speckle noise in synthetic aperture radar (SAR) images, a product sparse representation (PSR) algorithm is proposed to achieve SAR target configuration recognition. To extract the essential characteristics of SAR images, the product model is utilized to describe SAR images. The advantages of sparse representation and the product model are combined to realize a more accurate sparse representation of the SAR image. Moreover, in order to weaken the influences of the speckle noise on recognition, the speckle noise of SAR images is modeled by the Gamma distribution, and the sparse vector of the SAR image is obtained from q statistical standpoint. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) database. The experimental results validate the effectiveness and robustness of the proposed algorithm, which can achieve higher recognition rates than some of the state-of-the-art algorithms under different circumstances.

Study on Synthetic Aperture Radar Image Denoising Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1734-1737
Author(s):  
Wu Fen Chen ◽  
Ai Lian Liu ◽  
Jia Jia Xia ◽  
Chao Lei Duan ◽  
Song Song Yang ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Median Filter ◽  
Speckle Noise ◽  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Median Filtering ◽  
Soft Threshold ◽  
Aperture Radar ◽  
Improved Algorithm

In synthetic aperture radar (SAR) inherent speckle will affect the legibility of image details; give the image target detection adverse effects. In order to reduce the SAR image speckles noise, this article provided an improved algorithm based on median filter and wavelet semi-soft threshold shrinkage. First, reduced the SAR image speckle with median filtering method, then, with the filtered image, filtering the image with wavelet half soft threshold value contraction algorithm to noise, Simulation results show that the algorithm based on median filtering and improved algorithm of wavelet half soft threshold shrinkage of SAR image can better remove the speckle noise of the SAR image, while keep better edge, in Equivalent Number of Looks (ENL) and edge keep ability (FOM) aspects, it would be better than median filtering.

scholarly journals Despeckling and Analysing Land Use Land Cover of Synthetic Aperture Radar Image

2019 ◽  
Vol 8 (10) ◽  
pp. 2827-2831
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Speckle Noise ◽  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Land Use Land Cover ◽  
Sar Imagery ◽  
Aperture Radar

In Image processing, Synthetic Aperture Radar(SAR) images are inherently affected by speckle noise, which visually degrades the appearance of the images and may severely affect the quality of SAR image interpretation tasks like object detection or target detection, instance segmentation and image analysis. Hence SAR image Despeckling becomes a hot research issue. In this paper, the proposed method Total Variation (TV) Denoising is used to address this issue. It is applied to SAR imagery to decrease the noise. It is a filtering method which works efficiently. The process of decreasing the speckle noise is known as Despeckling. When there is noise in the image the actual data is affected. The actual meaning of noise is unwanted signal. Noise is an undesirable by-product in an image that disturbs the original image. On removal of noise, it results in the noise free SAR image. The Land Use Land Cover (LULC) analysis of a SAR image can be accurate when there is no noise in the SAR image. Therefore the main aim of this paper is to analyze the land use and land cover (LULC) in the despeckled high resolution image.

SAR Image Registration Using Ratio Mutual Information

2012 ◽  
Vol 241-244 ◽  
pp. 2630-2637
Author(s):  
Chun Rong Wei ◽  
Chu He ◽  
Hong Sun
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Speckle Noise ◽  
Synthetic Aperture Radar Image ◽  
Reference Image ◽  
Sar Image ◽  
Local Maxima ◽  
Characteristic Quantity ◽  
Registration Algorithm ◽  
Image Registration Algorithm

In order to reduce the noise sensitivity of the SAR (synthetic aperture radar) image registration, a image registration algorithm which basing on the ratio mutual information (RatioMI) is proposed in this paper. Firstly, the ratio images of the reference image and the floating image are gotten by using the ratio operator, and then take the two ratio images as a similar characteristic quantity to construct the similarity measure function which was used in the optimization process of the image registration experiment. The experimental results of the SAR image registration show that the new registration algorithm which based on the RatioMI is effectively in avoiding the local maxima point problems causing by speckle noise.

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.

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.

Feature Analysis and Selection of SAR Image Based on Contourlet Transform

2014 ◽  
Vol 631-632 ◽  
pp. 431-435
Author(s):  
Shi Qi Huang ◽  
Pei Feng Su ◽  
Yi Ting Wang
Keyword(s):  
Microwave Remote Sensing ◽  
Speckle Noise ◽  
Image Features ◽  
Contourlet Transform ◽  
Synthetic Aperture ◽  
Directional Sensitivity ◽  
Sar Image ◽  
Multi Scale ◽  
Decomposition Structure ◽  
Selection Of

Synthetic aperture radar (SAR) is a sort of microwave remote sensing imaging radar, which has much advantage. But it also has much shortcoming, such as speckle noise and directional sensitivity. Reducing impact of them to SAR image processing and applications is an important content, especially, extracting features for ground objects. Contourlet transform is a kind of multi-scale and multi-direction transform theory, and it is a sparse representation mode, too. This paper mainly studied Contourlet transform theory and its decomposition structure, and then it was used to extract SAR image features. Experimental results show that Contourlet transform can effetely extract SAR image features.

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