scholarly journals An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning

2021 ◽  
Vol 13 (18) ◽  
pp. 3636
Author(s):  
Ye Yuan ◽  
Yanxia Wu ◽  
Yan Fu ◽  
Yulei Wu ◽  
Lidan Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Noise Suppression ◽  
Speckle Noise ◽  
Training Data ◽  
Superior Performance ◽  
Sar Image ◽  
Sar Images ◽  
Image Despeckling ◽  
Image Pairs

As one of the main sources of remote sensing big data, synthetic aperture radar (SAR) can provide all-day and all-weather Earth image acquisition. However, speckle noise in SAR images brings a notable limitation for its big data applications, including image analysis and interpretation. Deep learning has been demonstrated as an advanced method and technology for SAR image despeckling. Most existing deep-learning-based methods adopt supervised learning and use synthetic speckled images to train the despeckling networks. This is because they need clean images as the references, and it is hard to obtain purely clean SAR images in real-world conditions. However, significant differences between synthetic speckled and real SAR images cause the domain gap problem. In other words, they cannot show superior performance for despeckling real SAR images as they do for synthetic speckled images. Inspired by recent studies on self-supervised denoising, we propose an advanced SAR image despeckling method by virtue of Bernoulli-sampling-based self-supervised deep learning, called SSD-SAR-BS. By only using real speckled SAR images, Bernoulli-sampled speckled image pairs (input–target) were obtained as the training data. Then, a multiscale despeckling network was trained on these image pairs. In addition, a dropout-based ensemble was introduced to boost the network performance. Extensive experimental results demonstrated that our proposed method outperforms the state-of-the-art for speckle noise suppression on both synthetic speckled and real SAR datasets (i.e., Sentinel-1 and TerraSAR-X).

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 Nonlocal CNN SAR Image Despeckling

2020 ◽  
Vol 12 (6) ◽  
pp. 1006 ◽  
Author(s):  
Davide Cozzolino ◽  
Luisa Verdoliva ◽  
Giuseppe Scarpa ◽  
Giovanni Poggi
Keyword(s):  
Deep Learning ◽  
Weighted Average ◽  
Sar Image ◽  
Sar Images ◽  
Nonlocal Means ◽  
Analysis Window ◽  
Account Information ◽  
Measure Of Similarity ◽  
Image Despeckling ◽  
Insight Into

We propose a new method for SAR image despeckling, which performs nonlocal filtering with a deep learning engine. Nonlocal filtering has proven very effective for SAR despeckling. The key idea is to exploit image self-similarities to estimate the hidden signal. In its simplest form, pixel-wise nonlocal means, the target pixel is estimated through a weighted average of neighbors, with weights chosen on the basis of a patch-wise measure of similarity. Here, we keep the very same structure of plain nonlocal means, to ensure interpretability of results, but use a convolutional neural network to assign weights to estimators. Suitable nonlocal layers are used in the network to take into account information in a large analysis window. Experiments on both simulated and real-world SAR images show that the proposed method exhibits state-of-the-art performance. In addition, the comparison of weights generated by conventional and deep learning-based nonlocal means provides new insight into the potential and limits of nonlocal information for SAR 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 A Boosting SAR Image Despeckling Method Based on Non-Local Weighted Group Low-Rank Representation

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3448 ◽  
Author(s):  
Jing Fang ◽  
Shaohai Hu ◽  
Xiaole Ma
Keyword(s):  
Low Rank ◽  
Weighted Averaging ◽  
Superior Performance ◽  
Data Matrix ◽  
Sar Image ◽  
Sar Images ◽  
Structure Information ◽  
Non Local ◽  
Low Rank Representation ◽  
Image Despeckling

In this paper, we propose a boosting synthetic aperture radar (SAR) image despeckling method based on non-local weighted group low-rank representation (WGLRR). The spatial structure information of SAR images leads to the similarity of the patches. Furthermore, the data matrix grouped by the similar patches within the noise-free SAR image is often low-rank. Based on this, we use low-rank representation (LRR) to recover the noise-free group data matrix. To maintain the fidelity of the recovered image, we integrate the corrupted probability of each pixel into the group LRR model as a weight to constrain the fidelity of recovered noise-free patches. Each single patch might belong to several groups, so different estimations of each patch are aggregated with a weighted averaging procedure. The residual image contains signal leftovers due to the imperfect denoising, so we strengthen the signal by leveraging on the availability of the denoised image to suppress noise further. Experimental results on simulated and actual SAR images show the superior performance of the proposed method in terms of objective indicators and of perceived image quality.

Improved Semantic Segmentation of Water Bodies and Land in SAR Images Using Generative Adversarial Networks

2020 ◽  
Vol 14 (01) ◽  
pp. 55-69 ◽  
Author(s):  
M. M. Manohara Pai ◽  
Vaibhav Mehrotra ◽  
Ujjwal Verma ◽  
Radhika M. Pai
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Ease Of Use ◽  
Training Data ◽  
Superior Performance ◽  
Easy Access ◽  
Generative Adversarial Networks ◽  
Data Repository ◽  
Sar Images ◽  
Adversarial Networks

The availability of computationally efficient and powerful Deep Learning frameworks and high-resolution satellite imagery has created new approach for developing complex applications in the field of remote sensing. The easy access to abundant image data repository made available by different satellites of space agencies such as Copernicus, Landsat, etc. has opened various avenues of research in monitoring the world’s oceans, land, rivers, etc. The challenging research problem in this direction is the accurate identification and subsequent segmentation of surface water in images in the microwave spectrum. In the recent years, deep learning methods for semantic segmentation are the preferred choice given its high accuracy and ease of use. One major bottleneck in semantic segmentation pipelines is the manual annotation of data. This paper proposes Generative Adversarial Networks (GANs) on the training data (images and their corresponding labels) to create an enhanced dataset on which the networks can be trained, therefore, reducing human effort of manual labeling. Further, the research also proposes the use of deep-learning approaches such as U-Net and FCN-8 to perform an efficient segmentation of auto annotated, enhanced data of water body and land. The experimental results show that the U-Net model without GAN achieves superior performance on SAR images with pixel accuracy of 0.98 and F1 score of 0.9923. However, when augmented with GANs, the results saw a rise in these metrics with PA of 0.99 and F1 score of 0.9954.

scholarly journals deSpeckNet: Generalizing Deep Learning-Based SAR Image Despeckling

2020 ◽  
pp. 1-15
Author(s):  
Adugna G. Mullissa ◽  
Diego Marcos ◽  
Devis Tuia ◽  
Martin Herold ◽  
Johannes Reiche
Keyword(s):  
Deep Learning ◽  
Sar Image ◽  
Image Despeckling

scholarly journals Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

2021 ◽  
Vol 13 (7) ◽  
pp. 1236
Author(s):  
Yuanjun Shu ◽  
Wei Li ◽  
Menglong Yang ◽  
Peng Cheng ◽  
Songchen Han
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Change Detection ◽  
Detection Method ◽  
Training Data ◽  
Synthetic Aperture ◽  
Learning Method ◽  
Sar Images ◽  
Two Stage ◽  
Second Stage

Convolutional neural networks (CNNs) have been widely used in change detection of synthetic aperture radar (SAR) images and have been proven to have better precision than traditional methods. A two-stage patch-based deep learning method with a label updating strategy is proposed in this paper. The initial label and mask are generated at the pre-classification stage. Then a two-stage updating strategy is applied to gradually recover changed areas. At the first stage, diversity of training data is gradually restored. The output of the designed CNN network is further processed to generate a new label and a new mask for the following learning iteration. As the diversity of data is ensured after the first stage, pixels within uncertain areas can be easily classified at the second stage. Experiment results on several representative datasets show the effectiveness of our proposed method compared with several existing competitive methods.

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 SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy

2020 ◽  
Vol 12 (16) ◽  
pp. 2636
Author(s):  
Emanuele Dalsasso ◽  
Xiangli Yang ◽  
Loïc Denis ◽  
Florence Tupin ◽  
Wen Yang
Keyword(s):  
Neural Networks ◽  
Gaussian Noise ◽  
State Of The Art ◽  
Additive White Gaussian Noise ◽  
Hybrid Approach ◽  
White Gaussian Noise ◽  
Training Data ◽  
Speckle Reduction ◽  
Sar Image ◽  
Sar Images

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) images. Many different schemes have been proposed for the restoration of intensity SAR images. Among the different possible approaches, methods based on convolutional neural networks (CNNs) have recently shown to reach state-of-the-art performance for SAR image restoration. CNN training requires good training data: many pairs of speckle-free/speckle-corrupted images. This is an issue in SAR applications, given the inherent scarcity of speckle-free images. To handle this problem, this paper analyzes different strategies one can adopt, depending on the speckle removal task one wishes to perform and the availability of multitemporal stacks of SAR data. The first strategy applies a CNN model, trained to remove additive white Gaussian noise from natural images, to a recently proposed SAR speckle removal framework: MuLoG (MUlti-channel LOgarithm with Gaussian denoising). No training on SAR images is performed, the network is readily applied to speckle reduction tasks. The second strategy considers a novel approach to construct a reliable dataset of speckle-free SAR images necessary to train a CNN model. Finally, a hybrid approach is also analyzed: the CNN used to remove additive white Gaussian noise is trained on speckle-free SAR images. The proposed methods are compared to other state-of-the-art speckle removal filters, to evaluate the quality of denoising and to discuss the pros and cons of the different strategies. Along with the paper, we make available the weights of the trained network to allow its usage by other researchers.

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