scholarly journals Junction Point Detection Algorithm for SAR Image

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Jun Zhang ◽  
Tingjin Luo ◽  
Gui Gao ◽  
Lin Lian
Keyword(s):  
Detection Algorithm ◽  
Junction Point ◽  
Sar Image ◽  
Sar Images ◽  
Weighted Distance ◽  
Junction Points ◽  
Point Detection ◽  
New Formula ◽  
The Impact ◽  
Point Detector

In this paper, we propose a novel junction point detector based on an azimuth consensus for remote sensing images. To eliminate the impact of noise and some noncorrelated edges of SAR image, an azimuth consensus constraint is developed. In addition to detecting the locations of junctions at the subpixel level, this operator recognizes their structures as well. A new formula that includes a minimization criterion for the total weighted distance is proposed to compute the locations of junction points accurately. Compared with other well-known detectors, including Forstner, JUDOCA, and CPDA, the experimental results indicate that our operator outperforms them both in location accuracy of junction points and in angle accuracy of branch edges. Moreover, our method possesses satisfying robustness to the impact of noise and changes of the SAR images. Our operator can be potentially used to solve a number of problems in computer vision, such as SAR image registration, wide-baseline matching, and UAV navigation system.

A New Multi-Scale Harris Interesting Point Detector

2014 ◽  
Vol 602-605 ◽  
pp. 1950-1955
Author(s):  
Guang Xue Wang ◽  
Yong Chun Liu ◽  
Huan He
Keyword(s):  
Object Tracking ◽  
Detection Algorithm ◽  
Scene Analysis ◽  
Data Sets ◽  
Scale Level ◽  
Interesting Point ◽  
Multi Scale ◽  
The Stability ◽  
Point Detection ◽  
Point Detector

The interesting point is important features in images, which is frequently used for image registration, scene analysis, object tracking. Many algorithms for detecting the interesting points have been developed up to now. Among them, the Harris interesting point detector is proved most stable against rotation and noise, while it is also very sensitive to scale change. In order to solve this problem, a new multi-scale Harris interesting point detection algorithm is proposed in this paper. The algorithm consists of two main stages. In stage one, we build a multi-scale representation for Harris measure and find candidate interesting points at each scale level. In stage two, a novel measure is defined to measure the stability of each point. Based on the measure, the final interesting point is selected from candidates. The experiments on both optical and sar data sets have shown that, compared with stand Harris interesting point detector and some other multi-scale interesting point detectors, our algorithm is more robust.

scholarly journals Bankline detection of GF-3 SAR images based on shearlet

2021 ◽  
Vol 7 ◽  
pp. e611
Author(s):  
Zengguo Sun ◽  
Guodong Zhao ◽  
Marcin Woźniak ◽  
Rafał Scherer ◽  
Robertas Damaševičius
Keyword(s):  
Water Environment ◽  
Detection Algorithm ◽  
Morphological Processing ◽  
Map Matching ◽  
Sar Image ◽  
Sar Images ◽  
Multi Scale ◽  
Local Means ◽  
Active Imaging ◽  
Non Local

The GF-3 satellite is China’s first self-developed active imaging C-band multi-polarization synthetic aperture radar (SAR) satellite with complete intellectual property rights, which is widely used in various fields. Among them, the detection and recognition of banklines of GF-3 SAR image has very important application value for map matching, ship navigation, water environment monitoring and other fields. However, due to the coherent imaging mechanism, the GF-3 SAR image has obvious speckle, which affects the interpretation of the image seriously. Based on the excellent multi-scale, directionality and the optimal sparsity of the shearlet, a bankline detection algorithm based on shearlet is proposed. Firstly, we use non-local means filter to preprocess GF-3 SAR image, so as to reduce the interference of speckle on bankline detection. Secondly, shearlet is used to detect the bankline of the image. Finally, morphological processing is used to refine the bankline and further eliminate the false bankline caused by the speckle, so as to obtain the ideal bankline detection results. Experimental results show that the proposed method can effectively overcome the interference of speckle, and can detect the bankline information of GF-3 SAR image completely and smoothly.

scholarly journals A Complex Valued ResNet Network Based Object Detection Algorithm in SAR Images

2021 ◽  
Vol 24 (4) ◽  
pp. 392-400
Author(s):  
Insu Hwang
Keyword(s):  
Object Detection ◽  
Detection Algorithm ◽  
Aerial Image ◽  
Complex Data ◽  
Sar Image ◽  
Sar Images ◽  
Phase Data ◽  
Image Object Detection ◽  
Complex Valued ◽  
Image Object

Unlike optical equipment, SAR(Synthetic Aperture Radar) has the advantage of obtaining images in all weather, and object detection in SAR images is an important issue. Generally, deep learning-based object detection was mainly performed in real-valued network using only amplitude of SAR image. Since the SAR image is complex data consist of amplitude and phase data, a complex-valued network is required. In this paper, a complex-valued ResNet network is proposed. SAR image object detection was performed by combining the ROI transformer detector specialized for aerial image detection and the proposed complex-valued ResNet. It was confirmed that higher accuracy was obtained in complex-valued network than in existing real-valued network.

scholarly journals Simulation of a Wideband Radar Echo of a Target on a Dynamic Sea Surface

2021 ◽  
Vol 13 (16) ◽  
pp. 3186
Author(s):  
Wang-Qiang Jiang ◽  
Liu-Ying Wang ◽  
Xin-Zhuo Li ◽  
Gu Liu ◽  
Min Zhang
Keyword(s):  
Multiple Scattering ◽  
Simulation Method ◽  
Sea Surface ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Sar Images ◽  
Em Scattering ◽  
Scattering Effects ◽  
The Impact ◽  
Multiple Scattering Effects

Unlike a generally rough ground surface, the sea surface varies over time. To analyze the impact of the motion of sea waves on the synthetic aperture radar (SAR) image of a target, the wideband echo simulation method based on a frequency domain is used. For the wideband echo, the electromagnetic (EM) scattering properties of the main frequency components are analyzed by the simulation method. Based on the EM scattering properties, the echo can be accurately simulated by using the inverse fast Fourier transformation (IFFT). Combined with the flight path of the radar, the echo of each pulse can be simulated to obtain the SAR image. The correct evaluation of the EM scattering properties is indispensable to the acquisition of an accurate SAR image. For complex targets, such as ships, the multiple scattering effects have a significant influence on the EM scattering properties. Thus, a rectangular wave beam-based geometrical optics and physical optics (GO/PO) method is introduced to calculate the EM scattering properties, which is more efficient than the traditional GO/PO. The GO/PO method is suitable to simulate SAR images in which the EM scattering properties of each pulse need to be calculated. With these methods, the SAR images of the target on the sea surface are simulated. Based on the comparison of the SAR images between a static and dynamic sea surface, it is found that the region corresponding to the target is blurred and the texture of the dynamic sea is blurrier. The impact of multiple scattering and sea wave motion on target recognition are also analyzed with the SAR images that were generated under different conditions. Some strong scattering points appear when multiple scattering effects are considered. It is also found that the texture of the SAR images, corresponding to the sea surface, changes with the synthetic aperture time.

Despeckling of Sar Images Using Shrinkage of Two-Dimensional Discrete Orthonormal S-Transform

2020 ◽  
pp. 2150023
Author(s):  
Priya R. Kamath ◽  
Kedarnath Senapati ◽  
P. Jidesh
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Relevant Information ◽  
Detection Algorithm ◽  
Two Dimensional ◽  
Sar Images ◽  
S Transform ◽  
Processing Step ◽  
Frequency Components ◽  
Shock Filter

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
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.

scholarly journals Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism

2021 ◽  
Vol 13 (14) ◽  
pp. 2686
Author(s):  
Di Wei ◽  
Yuang Du ◽  
Lan Du ◽  
Lu Li
Keyword(s):  
Feature Extraction ◽  
Supervised Learning ◽  
Target Detection ◽  
Scene Recognition ◽  
Attention Mechanism ◽  
Sar Image ◽  
Target Level ◽  
Sar Images ◽  
Training Samples ◽  
Pass Through

The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to targets in SAR images with complex scenes, making the target detection task very difficult. Therefore, a SAR target detection network based on a semi-supervised learning and attention mechanism is proposed in this paper. Since the image-level label simply marks whether the image contains the target of interest or not, which is easier to be labeled than the target-level label, the proposed method uses a small number of target-level labeled training samples and a large number of image-level labeled training samples to train the network with a semi-supervised learning algorithm. The proposed network consists of a detection branch and a scene recognition branch with a feature extraction module and an attention module shared between these two branches. The feature extraction module can extract the deep features of the input SAR images, and the attention module can guide the network to focus on the target of interest while suppressing the clutter. During the semi-supervised learning process, the target-level labeled training samples will pass through the detection branch, while the image-level labeled training samples will pass through the scene recognition branch. During the test process, considering the help of global scene information in SAR images for detection, a novel coarse-to-fine detection procedure is proposed. After the coarse scene recognition determining whether the input SAR image contains the target of interest or not, the fine target detection is performed on the image that may contain the target. The experimental results based on the measured SAR dataset demonstrate that the proposed method can achieve better performance than the existing methods.

scholarly journals High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

2021 ◽  
Vol 13 (10) ◽  
pp. 1909
Author(s):  
Jiahuan Jiang ◽  
Xiongjun Fu ◽  
Rui Qin ◽  
Xiaoyan Wang ◽  
Zhifeng Ma
Keyword(s):  
Deep Learning ◽  
Gpu Computing ◽  
Hot Spot ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Sar Image ◽  
Marine Monitoring ◽  
Ship Detection

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

scholarly journals Seasonal variability of SST fronts and winds on the southeastern continental shelf of Brazil

2019 ◽  
Vol 69 (11-12) ◽  
pp. 1387-1399 ◽  
Author(s):  
Huan-Huan Chen ◽  
Yiquan Qi ◽  
Yuntao Wang ◽  
Fei Chai
Keyword(s):  
Continental Shelf ◽  
Wind Stress ◽  
Seasonal Variability ◽  
Detection Algorithm ◽  
Coupling Coefficients ◽  
Seafloor Topography ◽  
Wind Stress Curl ◽  
Large Coupling ◽  
The Impact ◽  
Sst Fronts

Abstract Fourteen years (September 2002 to August 2016) of high-resolution satellite observations of sea surface temperature (SST) data are used to describe the frontal pattern and frontogenesis on the southeastern continental shelf of Brazil. The daily SST fronts are obtained using an edge-detection algorithm, and the monthly frontal probability (FP) is subsequently calculated. High SST FPs are mainly distributed along the coast and decrease with distance from the coastline. The results from empirical orthogonal function (EOF) decompositions reveal strong seasonal variability of the coastal SST FP with maximum (minimum) in the astral summer (winter). Wind plays an important role in driving the frontal activities, and high FPs are accompanied by strong alongshore wind stress and wind stress curl. This is particularly true during the summer, when the total transport induced by the alongshore component of upwelling-favorable winds and the wind stress curl reaches the annual maximum. The fronts are influenced by multiple factors other than wind forcing, such as the orientation of the coastline, the seafloor topography, and the meandering of the Brazil Current. As a result, there is a slight difference between the seasonality of the SST fronts and the wind, and their relationship was varying with spatial locations. The impact of the air-sea interaction is further investigated in the frontal zone, and large coupling coefficients are found between the crosswind (downwind) SST gradients and the wind stress curl (divergence). The analysis of the SST fronts and wind leads to a better understanding of the dynamics and frontogenesis off the southeastern continental shelf of Brazil, and the results can be used to further understand the air-sea coupling process at regional level.

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