scholarly journals An Adaptive Hierarchical Detection Method for Ship Targets in High-Resolution SAR Images

2020 ◽  
Vol 12 (2) ◽  
pp. 303 ◽  
Author(s):  
Yi Liang ◽  
Kun Sun ◽  
Yugui Zeng ◽  
Guofei Li ◽  
Mengdao Xing
Keyword(s):  
Detection Method ◽  
Estimation Method ◽  
Kernel Density ◽  
Image Resolution ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Airborne Sar ◽  
Visual Attention Mechanism ◽  
Synthetic Aperture Radars ◽  
Coarse To Fine

With the improvement of image resolution in synthetic aperture radars (SARs), sea clutter characteristics become more complex, which poses new challenges to traditional ship target detection missions. In this paper, to detect ship targets quickly and efficiently in a complex background, we propose an adaptive hierarchical detection method based on a coarse-to-fine mechanism. This method constructs a new visual attention mechanism to strengthen ship targets and obtain the candidate targets adaptively by the means dichotomy method. On this basis, the precise detection results of the targets are obtained using the speed block kernel density estimation method, which maintains constant false alarm characteristics. Compared with existing methods, the adaptive hierarchical detection method has simple, fast, and accurate characteristics. Experiments based on GF-III satellite and airborne SAR datasets are presented to demonstrate the effectiveness of the proposed method.

An Efficient Segmentation Method for Mariculture Areas on Airborne SAR Images

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.

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.

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.

scholarly journals N-YOLO: A SAR Ship Detection Using Noise-Classifying and Complete-Target Extraction

2021 ◽  
Vol 13 (5) ◽  
pp. 871
Author(s):  
Gang Tang ◽  
Yichao Zhuge ◽  
Christophe Claramunt ◽  
Shaoyang Men
Keyword(s):  
Noise Level ◽  
Detection Method ◽  
Synthetic Aperture ◽  
Target Area ◽  
Original Image ◽  
Sar Images ◽  
Ship Detection ◽  
Target Extraction ◽  
High Resolution Images ◽  
Potential Area

High-resolution images provided by synthetic aperture radar (SAR) play an increasingly important role in the field of ship detection. Numerous algorithms have been so far proposed and relative competitive results have been achieved in detecting different targets. However, ship detection using SAR images is still challenging because these images are still affected by different degrees of noise while inshore ships are affected by shore image contrasts. To solve these problems, this paper introduces a ship detection method called N-YOLO, which based on You Only Look Once (YOLO). The N-YOLO includes a noise level classifier (NLC), a SAR target potential area extraction module (STPAE) and a YOLOv5-based detection module. First, NLC derives and classifies the noise level of SAR images. Secondly, the STPAE module is composed by a CA-CFAR and expansion operation, which is used to extract the complete region of potential targets. Thirdly, the YOLOv5-based detection module combines the potential target area with the original image to get a new image. To evaluate the effectiveness of the N-YOLO, experiments are conducted using a reference GaoFen-3 dataset. The detection results show that competitive performance has been achieved by N-YOLO in comparison with several CNN-based algorithms.

scholarly journals Accurate detection method of moving target based on sequential SAR images with multiple azimuth squint angles

2021 ◽  
Vol 2083 (3) ◽  
pp. 032051
Author(s):  
Shiqi Yang ◽  
Yang Liu ◽  
Peili Xi ◽  
Chunsheng Li ◽  
Wei Yang ◽  
...  
Keyword(s):  
False Alarm ◽  
Detection Method ◽  
Synthetic Aperture ◽  
Moving Target ◽  
Constant False Alarm Rate ◽  
Moving Targets ◽  
Sar Images ◽  
Block Level ◽  
Detection Block ◽  
Motion Characteristics

Abstract In this paper, a novel moving target detection method for sequential Synthetic Aperture Radar (SAR) images with different azimuth-squint angles is proposed. In sequential SAR images, due to the movement of the target, the imaging position of moving targets among different frames differs. The method proposed in this paper uses this kind of motion characteristics to achieve the detection of moving targets in multi-frame SAR images. This algorithm can be divided into two parts: block-level detection and pixel-level detection. Block-level detection is achieved by stacked denoising autoencoders to extract the high-dimensional features of the moving target. Pixel-level detection consists of Local Binary Similarity Patterns (LBSP) coding as well as grayscale background subtraction. Pixel-level detection only needs to consider the pixels of foreground image pieces which contain moving targets. This method can not only increase the detection speed, but also suppress the false alarm problem caused by clutter. Experiments are carried out for verifying the validation of the method and the comparison are made between the proposed method and the traditional Constant False Alarm Rate (CFAR) algorithm.

scholarly journals Reduction of Spatially Correlated Speckle in Textured SAR Images

2021 ◽  
pp. 319-327
Author(s):  
Oleksii Rubel ◽  
Vladimir Lukin ◽  
Sergiy Krivenko ◽  
Vladimir Pavlikov ◽  
Simeon Zhyla ◽  
...  
Keyword(s):  
Complex Structure ◽  
Noise Removal ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Spatially Correlated ◽  
Sensing Systems ◽  
Remote Sensing Systems ◽  
Non Gaussian ◽  
Synthetic Aperture Radars ◽  
Sentinel 2

Synthetic aperture radars (SARs) provide a lot of images that can be used for numerous applications. A problem with acquired images is that they are corrupted by speckle which is a noise-like phenomenon with multiplicative nature. In addition, speckle is non-Gaussian and it is often spatially correlated. A typical task in SAR image processing is despeckling and many methods have been already proposed. However, most of them do not take noise spatial correlation into account during denoising. In this paper, we show how this can be done in despeckling based on discrete cosine transform. The use of frequency-dependent thresholds leads to sufficient improvement of denoising efficiency in terms of visual quality metrics. Moreover, we consider quite complex structure texture images for which noise removal is usually problematic and can lead to information loss. Comparison to the well-known local statistic Lee and Frost filters, extended DCT-based filter is carried out for different remote sensing systems including Sentinel-1 and Sentinel-2.

scholarly journals Possibilities and Limits of Synthetic Aperture Radar for Snow and Glacier Surveying

1987 ◽  
Vol 9 ◽  
pp. 195-199 ◽  
Author(s):  
H. Rott ◽  
C. Mätzler
Keyword(s):  
Optical Sensors ◽  
Swiss Alps ◽  
Synthetic Aperture ◽  
Microwave Measurements ◽  
Sar Images ◽  
Spectral Behaviour ◽  
Physical Background ◽  
Airborne Sar ◽  
Microwave Sensors ◽  
Snow And Ice

The physical background for the interpretation of microwave measurements on snow and ice is summarized. The angular and spectral behaviour of backscattering is shown for various snow types based on measurements carried out in the Swiss Alps. The information content of SAR images in regard to snow and glacier applications is discussed, and examples are shown for Seasat SAR and airborne SAR images. The preliminary specifications are given for an optimum SAR system for snow and glacier monitoring. The main advantage of SAR is due to its weather independence; for special applications the SAR information on the physical state of snow and ice may be of interest. Future SAR sensors can become important components in a snow and glacier monitoring system, but in order to fulfil all tasks, high-resolution optical sensors and improved passive microwave sensors will also be required.

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