SAR image despeckling based on variance constrained convolutional neural network

2018 ◽  
Author(s):  
Jun Wang ◽  
Peng Lei ◽  
Tong Zheng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sar Image ◽  
Image Despeckling

scholarly journals SAR Image Despeckling Using a Convolutional Neural Network

2017 ◽  
Vol 24 (12) ◽  
pp. 1763-1767 ◽  
Author(s):  
Puyang Wang ◽  
He Zhang ◽  
Vishal M. Patel
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sar Image ◽  
Image Despeckling

scholarly journals Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis

2021 ◽  
Vol 13 (17) ◽  
pp. 3444
Author(s):  
Hao Wang ◽  
Zhendong Ding ◽  
Xinyi Li ◽  
Shiyu Shen ◽  
Xiaodong Ye ◽  
...  
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
Convolutional Neural Network ◽  
Speckle Noise ◽  
Activation Function ◽  
Google Earth ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Sar Images ◽  
Image Despeckling

Synthetic aperture radar (SAR) images are often disturbed by speckle noise, making SAR image interpretation tasks more difficult. Therefore, speckle suppression becomes a pre-processing step. In recent years, approaches based on convolutional neural network (CNN) achieved good results in synthetic aperture radar (SAR) images despeckling. However, these CNN-based SAR images despeckling approaches usually require large computational resources, especially in the case of huge training data. In this paper, we proposed a SAR image despeckling method using a CNN platform with a new learnable spatial activation function, which required significantly fewer network parameters without incurring any degradation in performance over the state-of-the-art despeckling methods. Specifically, we redefined the rectified linear units (ReLU) function by adding a convolutional kernel to obtain the weight map of each pixel, making the activation function learnable. Meanwhile, we designed several experiments to demonstrate the advantages of our method. In total, 400 images from Google Earth comprising various scenes were selected as a training set in addition to 10 Google Earth images including athletic field, buildings, beach, and bridges as a test set, which achieved good despeckling effects in both visual and index results (peak signal to noise ratio (PSNR): 26.37 ± 2.68 and structural similarity index (SSIM): 0.83 ± 0.07 for different speckle noise levels). Extensive experiments were performed on synthetic and real SAR images to demonstrate the effectiveness of the proposed method, which proved to have a superior despeckling effect and higher ENL magnitudes than the existing methods. Our method was applied to coniferous forest, broad-leaved forest, and conifer broad-leaved mixed forest and proved to have a good despeckling effect (PSNR: 23.84 ± 1.09 and SSIM: 0.79 ± 0.02). Our method presents a robust framework inspired by the deep learning technology that realizes the speckle noise suppression for various remote sensing images.

scholarly journals Contourlet-CNN for SAR Image Despeckling

2021 ◽  
Vol 13 (4) ◽  
pp. 764
Author(s):  
Gang Liu ◽  
Hongzhaoning Kang ◽  
Quan Wang ◽  
Yumin Tian ◽  
Bo Wan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Features ◽  
Image Resolution ◽  
Speckle Reduction ◽  
Sar Image ◽  
Stream Structure ◽  
Optical Images ◽  
Image Despeckling ◽  
Image Detail

A multiscale and multidirectional network named the Contourlet convolutional neural network (CCNN) is proposed for synthetic aperture radar (SAR) image despeckling. SAR image resolution is not higher than that of optical images. If the network depth is increased blindly, the SAR image detail information flow will become quite weak, resulting in severe vanishing/exploding gradients. In this paper, a multiscale and multidirectional convolutional neural network is constructed, in which a single-stream structure of convolutional layers is replaced with a multiple-stream structure to extract image features with multidirectional and multiscale properties, thus significantly improving the despeckling performance. With the help of the Contourlet, the CCNN is designed with multiple independent subnetworks to respectively capture abstract features of an image in a certain frequency and direction band. The CCNN can increase the number of convolutional layers by increasing the number of subnetworks, which makes the CCNN not only have enough convolutional layers to capture the SAR image features, but also overcome the problem of vanishing/exploding gradients caused by deepening the networks. Extensive quantitative and qualitative evaluations of synthetic and real SAR images show the superiority of our proposed method over the state-of-the-art speckle reduction method.

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