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

2021 ◽  
Vol 10 (1) ◽  
pp. 127-134
Author(s):  
Anton V. Kvasnov ◽  
Vyacheslav P. Shkodyrev
Keyword(s):  
Synthetic Aperture Radar ◽  
Principal Component ◽  
Synthetic Aperture ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Aspect Angle ◽  
Radar Images ◽  
Artificial Neural ◽  
Aperture Radar

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.

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

2020 ◽  
Author(s):  
Aron Sommer
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Computational Costs ◽  
Radar Images ◽  
Open Sea ◽  
Sharp Image ◽  
Autofocus Algorithm ◽  
Rough Sea ◽  
Aperture Radar

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

2021 ◽  
Vol 13 (24) ◽  
pp. 5091
Author(s):  
Jinxiao Wang ◽  
Fang Chen ◽  
Meimei Zhang ◽  
Bo Yu
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Glacial Lake ◽  
Landsat 8 ◽  
Glacial Lakes ◽  
Sar Images ◽  
Radar Images ◽  
Optical Images ◽  
Sar Data ◽  
Aperture Radar

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.

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

2010 ◽  
Vol 138 (2) ◽  
pp. 475-496 ◽  
Author(s):  
Werner Alpers ◽  
Jen-Ping Chen ◽  
Chia-Jung Pi ◽  
I-I. Lin
Keyword(s):  
Synthetic Aperture Radar ◽  
East Coast ◽  
Physical Mechanism ◽  
Mesoscale Model ◽  
Synthetic Aperture ◽  
Katabatic Wind ◽  
Mountain Range ◽  
Sar Images ◽  
Radar Images ◽  
Aperture Radar

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.

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

2021 ◽  
Vol 13 (22) ◽  
pp. 4637
Author(s):  
Runzhi Jiao ◽  
Qingsong Wang ◽  
Tao Lai ◽  
Haifeng Huang
Keyword(s):  
Synthetic Aperture Radar ◽  
Stable Matching ◽  
Synthetic Aperture ◽  
Topological Isomorphism ◽  
Matching Method ◽  
Sar Images ◽  
Keypoint Detection ◽  
Radar Images ◽  
Geometric Distortions ◽  
Aperture Radar

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.

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