Backprojection Autofocus of Large Ships with Arbitrary Motion for Synthetic Aperture Radar

Synthetic Aperture ◽

Sar Images ◽

Computational Costs ◽

Radar Images ◽

Open Sea ◽

Sharp Image ◽

Autofocus Algorithm ◽

Rough Sea ◽

Radar images of the open sea taken by airborne synthetic aperture radar (SAR) show typically several smeared ships. Due to their non-linear motions on a rough sea, these ships are smeared beyond recognition, such that their images are useless for classification or identification tasks. The ship imaging algorithm presented in this thesis consists of a fast image reconstruction using the fast factorized backprojection algorithm and an extended autofocus algorithm of large moving ships. This thesis analysis the factorization parameters of the fast factorized backprojection algorithm and describes how to choose them nearoptimally in order to reconstruct SAR images with minimal computational costs and without any loss of quality. Furthermore, this thesis shows how to estimate and compensate for the translation, the rotation and the deformation of a large arbitrarily moving ship in order to reconstruct a sharp image of the ship. The proposed autofocus technique generates images in which the ...

ACFNet: A Feature Fusion Network for Glacial Lake Extraction Based on Optical and Synthetic Aperture Radar Images

Remote Sensing ◽

10.3390/rs13245091 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5091

Author(s):

Jinxiao Wang ◽

Fang Chen ◽

Meimei Zhang ◽

Bo Yu

Keyword(s):

Synthetic Aperture ◽

Glacial Lake ◽

Landsat 8 ◽

Glacial Lakes ◽

Sar Images ◽

Radar Images ◽

Optical Images ◽

Sar Data ◽

Glacial lake extraction is essential for studying the response of glacial lakes to climate change and assessing the risks of glacial lake outburst floods. Most methods for glacial lake extraction are based on either optical images or synthetic aperture radar (SAR) images. Although deep learning methods can extract features of optical and SAR images well, efficiently fusing two modality features for glacial lake extraction with high accuracy is challenging. In this study, to make full use of the spectral characteristics of optical images and the geometric characteristics of SAR images, we propose an atrous convolution fusion network (ACFNet) to extract glacial lakes based on Landsat 8 optical images and Sentinel-1 SAR images. ACFNet adequately fuses high-level features of optical and SAR data in different receptive fields using atrous convolution. Compared with four fusion models in which data fusion occurs at the input, encoder, decoder, and output stages, two classical semantic segmentation models (SegNet and DeepLabV3+), and a recently proposed model based on U-Net, our model achieves the best results with an intersection-over-union of 0.8278. The experiments show that fully extracting the characteristics of optical and SAR data and appropriately fusing them are vital steps in a network’s performance of glacial lake extraction.

On the Origin of Atmospheric Frontal Lines off the East Coast of Taiwan Observed on Spaceborne Synthetic Aperture Radar Images

Monthly Weather Review ◽

10.1175/2009mwr2987.1 ◽

2010 ◽

Vol 138 (2) ◽

pp. 475-496 ◽

Cited By ~ 7

Author(s):

Werner Alpers ◽

Jen-Ping Chen ◽

Chia-Jung Pi ◽

I-I. Lin

Keyword(s):

East Coast ◽

Physical Mechanism ◽

Mesoscale Model ◽

Synthetic Aperture ◽

Katabatic Wind ◽

Mountain Range ◽

Sar Images ◽

Radar Images ◽

Abstract Frontal lines having offshore distances typically between 40 and 80 km are often visible on synthetic aperture radar (SAR) images acquired over the east coast of Taiwan by the European Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2) and Envisat. In a previous paper the authors showed that they are of atmospheric and not of oceanic origin; however, in that paper they did not give a definite answer to the question of which physical mechanism causes them. In this paper the authors present simulations carried out with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model, which shows that the frontal lines are associated with a quasi-stationary low-level convergence zone generated by the dynamic interaction of onshore airflow of the synoptic-scale wind with the coastal mountain range of the island of Taiwan. Reversed airflow collides with the onshore-flowing air leading to an uplift of air, which is often accompanied by the formation of bands of increased cloud density and of rainbands. The physical mechanism causing the generation of the frontal lines is similar to the one responsible for the formation of cloud bands off the Island of Hawaii as described by Smolarkiewicz et al. Four SAR images are shown, one acquired by ERS-2 and three by Envisat, showing frontal lines at the east coast of Taiwan caused by this generation mechanism. For these events the recirculation pattern, as well as the frontal (or convective) lines observed, were reproduced quite well with the meteorological model. So, it is argued that the observed frontal lines are not seaward boundaries of (classical) barrier jets or of katabatic wind fields, which have characteristics that are quite different from the flow patterns around the east coast of Taiwan as indicated by the SAR images.

Multi-Hypothesis Topological Isomorphism Matching Method for Synthetic Aperture Radar Images with Large Geometric Distortion

Remote Sensing ◽

10.3390/rs13224637 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4637

Author(s):

Runzhi Jiao ◽

Qingsong Wang ◽

Tao Lai ◽

Haifeng Huang

Keyword(s):

Stable Matching ◽

Synthetic Aperture ◽

Topological Isomorphism ◽

Matching Method ◽

Sar Images ◽

Keypoint Detection ◽

Radar Images ◽

Geometric Distortions ◽

The dramatic undulations of a mountainous terrain will introduce large geometric distortions in each Synthetic Aperture Radar (SAR) image with different look angles, resulting in a poor registration performance. To this end, this paper proposes a multi-hypothesis topological isomorphism matching method for SAR images with large geometric distortions. The method includes the Ridge-Line Keypoint Detection (RLKD) and Multi-Hypothesis Topological Isomorphism Matching (MHTIM). Firstly, based on the analysis of the ridge structure, a ridge keypoint detection module and a keypoint similarity description method are designed, which aim to quickly produce a small number of stable matching keypoint pairs under large look angle differences and large terrain undulations. The keypoint pairs are further fed into the MHTIM module. Subsequently, the MHTIM method is proposed, which uses the stability and isomorphism of the topological structure of the keypoint set under different perspectives to generate a variety of matching hypotheses, and iteratively achieves the keypoint matching. This method uses both local and global geometric relationships between two keypoints, hence it achieving better performance compared with traditional methods. We tested our approach on both simulated and real mountain SAR images with different look angles and different elevation ranges. The experimental results demonstrate the effectiveness and stable matching performance of our approach.

Flood Mapping in the Coastal Region of Bangladesh Using Sentinel-1 SAR Images: A Case Study of Super Cyclone Amphan

Journal of the Civil Engineering Forum ◽

10.22146/jcef.64497 ◽

2021 ◽

Vol 7 (3) ◽

pp. 267

Author(s):

Pollen Chakma ◽

Aysha Akter

Keyword(s):

Coastal Region ◽

Flood Damage ◽

Synthetic Aperture ◽

Flood Mapping ◽

Sar Images ◽

Radar Images ◽

Super Cyclone ◽

Floods are triggered by water overflow into drylands from several sources, including rivers, lakes, oceans, or heavy rainfall. Near real-time (NRT) flood mapping plays an important role in taking strategic measures to reduce flood damage after a flood event. There are many satellite imagery based remote sensing techniques that are widely used to generate flood maps. Synthetic aperture radar (SAR) images have proven to be more effective in flood mapping due to its high spatial resolution and cloud penetration capacity. This case study is focused on the super cyclone, commonly known as Amphan, stemming from the west Bengal-Bangladesh coast across the Sundarbans on 20 May 2020, with a wind speed between 155 -165 gusting up to 185 . The flooding extent is determined by analyzing the pre and post-event synthetic aperture radar images, using the change detection and thresholding (CDAT) method. The results showed an inundated landmass of 2146 on 22 May 2020, excluding Sundarban. However, the area became 1425 about a week after the event, precisely on 28 May 2020 . This persistency generated a more severe and intense flood, due to the broken embankments. Furthermore, 13 out of 19 coastal districts were affected by the flooding, while 8 were highly inundated, including Bagerhat, Pirojpur, Satkhira, Khulna, Barisal, Jhalokati, Patuakhali and Barguna. These findings were subsequently compared with an inundation map created with a validation survey immediately after the event and also with the disposed location using a machine learning-based image classification technique. Consequently, the comparison showed a close similarity between the inundation scenario and the flood reports from the secondary sources. This circumstance envisages the significant role of CDAT application in providing relevant information for an effective decision support system.

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

Study of the Effects of Target Geometry on Synthetic Aperture Radar Images using Simulation Studies

10.5194/isprsarchives-xl-8-1227-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 1227-1231

Author(s):

K. Tummala ◽

A. K. Jha ◽

S. Kumar

Keyword(s):

Synthetic Aperture ◽

Target Area ◽

Simulation Algorithm ◽

Sar Image ◽

Sar Images ◽

Radar Images ◽

The Earth ◽

Simulated Images ◽

Synthetic aperture radar technology has revolutionized earth observation with very high resolutions of below 5m, making it possible to distinguish individual urban features like buildings and even cars on the surface of the earth. But, the difficulty in interpretation of these images has hindered their use. The geometry of target objects and their orientation with respect to the SAR sensor contribute enormously to unexpected signatures on SAR images. Geometry of objects can cause single, double or multiple reflections which, in turn, affect the brightness value on the SAR images. Occlusions, shadow and layover effects are present in the SAR images as a result of orientation of target objects with respect to the incident microwaves. Simulation of SAR images is the best and easiest way to study and understand the anomalies. This paper discusses synthetic aperture radar image simulation, with the study of effect of target geometry as the main aim. Simulation algorithm has been developed in the time domain to provide greater modularity and to increase the ease of implementation. This algorithm takes into account the sensor and target characteristics, their locations with respect to the earth, 3-dimensional model of the target, sensor velocity, and SAR parameters. two methods have been discussed to obtain position and velocity vectors of SAR sensor – the first, from the metadata of real SAR image used to verify the simulation algorithm, and the second, from satellite orbital parameters. Using these inputs, the SAR image coordinates and backscatter coefficients for each point on the target are calculated. The backscatter coefficients at target points are calculated based on the local incidence angles using Muhleman's backscatter model. The present algorithm has been successfully implemented on radarsat-2 image of San Francisco bay area. Digital elevation models (DEMs) of the area under consideration are used as the 3d models of the target area. DEMs of different resolutions have been used to simulate SAR images in order to study how the target models affect the accuracy of simulation algorithm. The simulated images have been compared with radarsat-2 images to observe the efficiency of the simulation algorithm in accurately representing the locations and extents of different objects in the target area. The simulated algorithm implemented in this paper has given satisfactory results as the simulated images accurately show the different features present in the DEM of the target area.

Self-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images

Remote Sensing ◽

10.3390/rs13214383 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4383

Author(s):

Gang Zhang ◽

Zhi Li ◽

Xuewei Li ◽

Sitong Liu

Keyword(s):

Real World ◽

Speckle Noise ◽

Synthetic Aperture ◽

Noisy Image ◽

Deep Convolutional Neural Networks ◽

Sar Images ◽

Radar Images ◽

Image Pairs ◽

Self-supervised method has proven to be a suitable approach for despeckling on synthetic aperture radar (SAR) images. However, most self-supervised despeckling methods are trained by noisy-noisy image pairs, which are constructed by using natural images with simulated speckle noise, time-series real-world SAR images or generative adversarial network, limiting the practicability of these methods in real-world SAR images. Therefore, in this paper, a novel self-supervised despeckling algorithm with an enhanced U-Net is proposed for real-world SAR images. Firstly, unlike previous self-supervised despeckling works, the noisy-noisy image pairs are generated from real-word SAR images through a novel generation training pairs module, which makes it possible to train deep convolutional neural networks using real-world SAR images. Secondly, an enhanced U-Net is designed to improve the feature extraction and fusion capabilities of the network. Thirdly, a self-supervised training loss function with a regularization loss is proposed to address the difference of target pixel values between neighbors on the original SAR images. Finally, visual and quantitative experiments on simulated and real-world SAR images show that the proposed algorithm notably removes speckle noise with better preserving features, which exceed several state-of-the-art despeckling methods.

Ship Detection Based on Information Theory and Segmentation from Synthetic Aperture Radar (SAR) Images

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f8465.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 2513-2517

Keyword(s):

Remote Sensing ◽

Satellite Images ◽

Synthetic Aperture ◽

Morphological Operations ◽

Sar Images ◽

Ship Detection ◽

Radar Images ◽

Vessel Detection ◽

Ship detection is a procedure which asserts in fields such as ocean and sea management, vessel detection, marine superintendence, and rein, and also can be applied to exclude extralegal actions. Remote sensing can be utilized as a potential tool for zonular and universal monitoring to attain the forenamed goals. Among the radar images, the precious datum from Synthetic Aperture Radar (SAR) is playing a serious duty in remote sensing. Howsoever, vessel detecting in heterogeneous and strong clutter is still a question in this regard. The letter points to a ship detection scheme for SAR images exploiting a segmentation-based morphological operation using entropy. In the presented scheme, the morphological operations are adopted to intercept the background and foreground in the satellite images. The method was implemented and tested on the homogenous, heterogeneous and strong clutter SAR images and the results are promising and showing that the proposed method can improve the vessel detection from homogenous and heterogeneous and strong clutter satellite images

Radar Target Recognition using Salient Keypoint Descriptors and Multitask Sparse Representation

10.20944/preprints201804.0251.v1 ◽

2018 ◽

Author(s):

Ayoub Karine ◽

Abdelmalek Toumi ◽

Ali Khenchaf ◽

Mohammed El Hassouni

Keyword(s):

Sparse Representation ◽

Computing Time ◽

Synthetic Aperture ◽

Target Area ◽

Scale Invariant ◽

Sar Images ◽

Radar Images ◽

Sift Keypoints ◽

In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images, we combine the scale-invariant feature transform (SIFT) and the saliency map. The goal of this combination is to reduce the SIFT keypoints and their time computing time by maintaining only those located in the target area (salient region). Then, we compute the feature vectors of the resulting salient SIFT keypoints (MSKD). This methodology is applied for both training and test images. The MSKD of the training images leads to construct the dictionary of a sparse convex optimization problem. To achieve the recognition, we adopt the MSRC taking into consideration each vector in the MSKD as a task. This classifier solves the sparse representation problem for each task over the dictionary and determines the class of the radar image according to all sparse reconstruction errors (residuals). The effectiveness of the proposed approach method has been demonstrated by a set of extensive empirical results on ISAR and SAR images databases. The results show the ability of our method to predict adequately the aircraft and the ground targets.

Removing Speckle Noise in Synthetic Aperture Radar Images using Combined Intensity Coherence Vector & Hybrid Filtering

Psychology and Education Journal ◽

10.17762/pae.v58i1.1497 ◽

2021 ◽

Vol 58 (1) ◽

pp. 4289-4295

Author(s):

Dr. D. Suresh Et al.

Keyword(s):

Speckle Noise ◽

Synthetic Aperture ◽

Sar Image ◽

Signal To Noise ◽

Sar Images ◽

Radar Images ◽

Hybrid Filtering ◽

Edge Based ◽

Noise will be unavoidable in image securing practice and denoising is a fundamental advance to recoup the image quality. The image of Synthetic Aperture Radar (SAR) is intrinsically misrepresented in dot noise that happens because of coherent nature of the dispersing wonders. Denoising SAR images target eliminating dot while safeguarding image highlights, for example, surface, edges, and point targets. The blend of nonlocal gathering and changed area filtering has coordinated the cutting edge denoising methods. Notwithstanding, this methodology makes an intense suspicion that image fix itself gives a brilliant guess on the genuine boundary, which prompts predisposition issue transcendently under genuine dot noise. Another impediment is that the for the most part utilized fix pre-determination techniques can't productively avoid the exceptions and harm the edges. The SAR image is infused with spot noise, and afterward edge based marker controlled watershed division is applied to recognize the homogeneous locales in SAR image. For every locale, the local pixels are distinguished by utilizing Intensity Coherence Vector (ICV) and are denoised autonomously by utilizing a half and half filtering, which contains the improved forms of ice, middle and mean channel. The exploratory outcomes show that the proposed strategy outflanks different techniques, for example, fix based filtering, non-nearby methods, wavelets and old style dot channels in wording higher wavelets signal-to-noise and edge conservation proportions relatively.

A classification technique of group objects by artificial neural networks using estimation of entropy on synthetic aperture radar images

Journal of Sensors and Sensor Systems ◽

10.5194/jsss-10-127-2021 ◽

2021 ◽

Vol 10 (1) ◽

pp. 127-134

Author(s):

Anton V. Kvasnov ◽

Vyacheslav P. Shkodyrev

Keyword(s):

Principal Component ◽

Synthetic Aperture ◽

Constant False Alarm Rate ◽

Sar Images ◽

Aspect Angle ◽

Radar Images ◽

Artificial Neural ◽

Abstract. The article discusses the method for the classification of non-moving group objects for information received from unmanned aerial vehicles (UAVs) by synthetic aperture radar (SAR). A theoretical approach to analysis of group objects can be estimated by cross-entropy using a naive Bayesian classifier. The entropy of target spots on SAR images revaluates depending on the altitude and aspect angle of a UAV. The paper shows that classification of the target for three classes able to predict with fair accuracy P=0,964 based on an artificial neural network. The study of results reveals an advantage compared with other radar recognition methods for a criterion of the constant false-alarm rate (PCFAR<0.01). The reliability was confirmed by checking the initial data using principal component analysis.