SAR-Based Flood Monitoring for Flatland with Frequently Fluctuating Water Surfaces: Proposal for the Normalized Backscatter Amplitude Difference Index (NoBADI)

Space-based synthetic aperture radar (SAR) is a powerful tool for monitoring flood conditions over large areas without the influence of clouds and daylight. Permanent water surfaces can be excluded by comparing SAR images with pre-flood images, but fluctuating water surfaces, such as those found in flat wetlands, introduce uncertainty into flood mapping results. In order to reduce this uncertainty, a simple method called Normalized Backscatter Amplitude Difference Index (NoBADI) is proposed in this study. The NoBADI is calculated from a post-flood SAR image of backscatter amplitude and multiple images on non-flooding conditions. Preliminary analysis conducted in the US state of Florida, which was affected by Hurricane Irma in September 2017, shows that surfaces frequently covered by water (more than 20% of available data) have been successfully excluded by means of C-/L-band SAR (HH, HV, VV, and VH polarizations). Although a simple comparison of pre-flood and post-flood images is greatly affected by the spatial distribution of the water surface in the pre-flood image, the NoBADI method reduces the uncertainty of the reference water surface. This advantage will contribute in making quicker decisions during crisis management.

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Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽

10.3390/s21051643 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1643

Author(s):

Ming Liu ◽

Shichao Chen ◽

Fugang Lu ◽

Mengdao Xing ◽

Jingbiao Wei

Keyword(s):

Synthetic Aperture Radar ◽

False Alarm ◽

False Alarm Rate ◽

Target Detection ◽

Synthetic Aperture ◽

False Alarms ◽

Sar Image ◽

Constant False Alarm Rate ◽

Sar Images ◽

Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

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

Information Technology And Control ◽

10.5755/j01.itc.49.3.23998 ◽

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.

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

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch028 ◽

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.

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Product Dictionary Learning-Based SAR Target Configuration Recognition

International Journal of Antennas and Propagation ◽

10.1155/2020/9704829 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Ming Liu ◽

Shichao Chen ◽

Fugang Lu ◽

Junsheng Liu

Keyword(s):

Synthetic Aperture Radar ◽

Sparse Representation ◽

Dictionary Learning ◽

Synthetic Aperture ◽

Sar Image ◽

Product Model ◽

Sar Images ◽

Stationary Target ◽

Target Configuration ◽

Key Factor

Dictionary construction is a key factor for the sparse representation- (SR-) based algorithms. It has been verified that the learned dictionaries are more effective than the predefined ones. In this paper, we propose a product dictionary learning (PDL) algorithm to achieve synthetic aperture radar (SAR) target configuration recognition. The proposed algorithm obtains the dictionaries from a statistical standpoint to enhance the robustness of the proposed algorithm to noise. And, taking the inevitable multiplicative speckle in SAR images into account, the proposed algorithm employs the product model to describe SAR images. A more accurate description of the SAR image results in higher recognition rates. The accuracy and robustness of the proposed algorithm are validated by the moving and stationary target acquisition and recognition (MSTAR) database.

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ANALYSIS OF SCATTERING COMPONENTS FROM FULLY POLARIMETRIC SAR IMAGES FOR IMPROVING ACCURACIES OF URBAN DENSITY ESTIMATION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-7-219-2016 ◽

2016 ◽

Vol III-7 ◽

pp. 219-226

Author(s):

J. Susaki

Keyword(s):

Normal Distribution ◽

Urban Areas ◽

Synthetic Aperture ◽

Sar Image ◽

Polarimetric Sar ◽

Sar Images ◽

Polarimetric Synthetic Aperture Radar ◽

Urban Density ◽

Concrete Blocks ◽

Image Pixels

In this paper, we analyze probability density functions (PDFs) of scatterings derived from fully polarimetric synthetic aperture radar (SAR) images for improving the accuracies of estimated urban density. We have reported a method for estimating urban density that uses an index Tv+c obtained by normalizing the sum of volume and helix scatterings Pv+c. Validation results showed that estimated urban densities have a high correlation with building-to-land ratios (Kajimoto and Susaki, 2013b; Susaki et al., 2014). While the method is found to be effective for estimating urban density, it is not clear why Tv+c is more effective than indices derived from other scatterings, such as surface or double-bounce scatterings, observed in urban areas. In this research, we focus on PDFs of scatterings derived from fully polarimetric SAR images in terms of scattering normalization. First, we introduce a theoretical PDF that assumes that image pixels have scatterers showing random backscattering. We then generate PDFs of scatterings derived from observations of concrete blocks with different orientation angles, and from a satellite-based fully polarimetric SAR image. The analysis of the PDFs and the derived statistics reveals that the curves of the PDFs of Pv+c are the most similar to the normal distribution among all the scatterings derived from fully polarimetric SAR images. It was found that Tv+c works most effectively because of its similarity to the normal distribution.

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Two-Stream Deep Fusion Network Based on VAE and CNN for Synthetic Aperture Radar Target Recognition

Remote Sensing ◽

10.3390/rs13204021 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4021

Author(s):

Lan Du ◽

Lu Li ◽

Yuchen Guo ◽

Yan Wang ◽

Ke Ren ◽

...

Keyword(s):

Target Recognition ◽

Synthetic Aperture ◽

Radar Target ◽

Sar Image ◽

Sar Images ◽

Radar Target Recognition ◽

Probabilistic Distribution ◽

Extraction Stage ◽

Different Characteristics ◽

Sar Target Recognition

Usually radar target recognition methods only use a single type of high-resolution radar signal, e.g., high-resolution range profile (HRRP) or synthetic aperture radar (SAR) images. In fact, in the SAR imaging procedure, we can simultaneously obtain both the HRRP data and the corresponding SAR image, as the information contained within them is not exactly the same. Although the information contained in the HRRP data and the SAR image are not exactly the same, both are important for radar target recognition. Therefore, in this paper, we propose a novel end-to-end two stream fusion network to make full use of the different characteristics obtained from modeling HRRP data and SAR images, respectively, for SAR target recognition. The proposed fusion network contains two separated streams in the feature extraction stage, one of which takes advantage of a variational auto-encoder (VAE) network to acquire the latent probabilistic distribution characteristic from the HRRP data, and the other uses a lightweight convolutional neural network, LightNet, to extract the 2D visual structure characteristics based on SAR images. Following the feature extraction stage, a fusion module is utilized to integrate the latent probabilistic distribution characteristic and the structure characteristic for the reflecting target information more comprehensively and sufficiently. The main contribution of the proposed method consists of two parts: (1) different characteristics from the HRRP data and the SAR image can be used effectively for SAR target recognition, and (2) an attention weight vector is used in the fusion module to adaptively integrate the different characteristics from the two sub-networks. The experimental results of our method on the HRRP data and SAR images of the MSTAR and civilian vehicle datasets obtained improvements of at least 0.96 and 2.16%, respectively, on recognition rates, compared with current SAR target recognition methods.

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

Remote Sensing ◽

10.3390/rs13183733 ◽

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.

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Impact of Method Noise on SAR Image Despeckling

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2020010104 ◽

2020 ◽

Vol 15 (1) ◽

pp. 52-63 ◽

Cited By ~ 3

Author(s):

Prabhishek Singh ◽

Raj Shree

Keyword(s):

Performance Metrics ◽

Synthetic Aperture ◽

Sar Image ◽

Sar Images ◽

Standard Methods ◽

Residual Image ◽

Method Noise ◽

Image Despeckling ◽

The Difference ◽

And Performance

This article introduces the concept, use and implementation of method noise in the field of synthetic aperture radar (SAR) image despeckling. Method noise has the capability to enhance the efficiency and performance of any despeckling algorithm. It is easy, efficient and enhanced way of improving the results. The difference between speckled image and despeckled image contains some residual image information which is due to the inefficiency of the denoising algorithm. This article will compare the results of some standard methods with and without the use of method noise and prove its efficiency and validity. It also shows its best use in different ways of denoising. The results will be compared on the basis of performance metrics like PSNR and SSIM. The concept of method noise is not restricted to only SAR images. It has vast usage and application. It can be used in any denoising procedure such as medical images, optical image etc. but this paper shows the experimental results only on the SAR images.

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An Efficient Segmentation Method for Mariculture Areas on Airborne SAR Images

Marine Technology Society Journal ◽

10.4031/mtsj.53.3.10 ◽

2019 ◽

Vol 53 (3) ◽

pp. 30-38

Author(s):

Houjun Wang ◽

Hui Liu ◽

Ning Ding ◽

Pingping Jing ◽

Guangyu Li

Keyword(s):

Image Data ◽

Synthetic Aperture ◽

Sar Image ◽

Adaptive Segmentation ◽

Segmentation Method ◽

Clustering Method ◽

Sar Images ◽

Local Patch ◽

Airborne Sar ◽

Coarse To Fine

AbstractIn this paper, the problems of mariculture area segmentation and corresponding area value estimations are investigated on the basis of airborne synthetic aperture radar (SAR) images. In order to deal with a limited amount of noisy airborne SAR image data in an efficient way, an effective coarse-to-fine approach is proposed, consisting of three major components, including (1) an adaptive segmentation method for each local patch to remove noise from the ocean background, (2) a dynamic coarse-to-fine clustering method for grouping pixels to achieve image segments, and (3) a polygon-fitting-based algorithm to obtain regular borders for each region and corresponding area value. Some feasible experiments are operated based on the restricted airborne SAR images, and the effectiveness of the proposed algorithm is validated in terms of the provided pixel level evaluation annotations.

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Two methods for firn-area and mass-balance monitoring of Svalbard glaciers with SAR satellite images

Journal of Glaciology ◽

10.3189/172756504781830286 ◽

2004 ◽

Vol 50 (168) ◽

pp. 116-128 ◽

Cited By ~ 13

Author(s):

Max König ◽

Jan-Gunnar Winther ◽

Jack Kohler ◽

Florian König

Keyword(s):

Mass Balance ◽

Satellite Images ◽

Synthetic Aperture ◽

Sar Image ◽

Negative Mass ◽

Sar Images ◽

Glacier Surface ◽

Absolute Mass ◽

Measured Mass ◽

Over Time

AbstractThis paper presents two methods for glacier monitoring on Svalbard using synthetic aperture radar (SAR) satellite images. Both methods were developed on glaciers in the Kongsfjorden area. The first method monitors the firn area extent and the firn line over time by thresholding and filtering the SAR image. Manual detection of the threshold is preferable, but using a constant threshold for all images also gives adequate results. A retreat of the firn-line position is visible, especially on Kongsvegen, corresponding to consecutive years of negative mass balance. The second method applies a k-means classification to three clusters on the glacier surface. The areal extent of the resulting class on the upper part of the glacier correlates remarkably well with the independently measured mass balance of Kongsvegen, having a correlation coefficient of around 0.89 for the various glaciers. This is because the snow from the accumulation area influences the k-means classification. Thus, on glaciers where mass-balance values are available, new mass-balance values can be predicted solely from SAR images. For glaciers where no mass balance is available, the area change cannot be calibrated to absolute mass-balance values, but relative changes can be predicted.

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