2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)最新文献_第7页

Surface Deformation Monitoring Induced by Volcanic Activity of Mount Agung, Indonesia, by PS-InSAR Using Sentinel-1 SAR from 2014-2021 基于Sentinel-1 SAR的2014-2021年印尼阿贡火山活动地表变形PS-InSAR监测

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688489

I. Nyoman Sudi Parwata, T. Osawa

{"title":"Surface Deformation Monitoring Induced by Volcanic Activity of Mount Agung, Indonesia, by PS-InSAR Using Sentinel-1 SAR from 2014-2021","authors":"I. Nyoman Sudi Parwata, T. Osawa","doi":"10.1109/APSAR52370.2021.9688489","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688489","url":null,"abstract":"Mount Agung is an active volcano located in Bali island, Indonesia, which had been dormant since 1963. However, Mount Agung began to show volcanic activity with severe earthquakes in August 2017. The intense seismicity peaked in early October 2017, and the volcano did not erupt until November 21, 2017. However, a violent explosive eruption with rapid lava-spewing occurred, forcing 140,000 people to evacuate between November 25 and 29, 2017. This study aims to monitor surface deformation induced by volcanic activity of Mount Agung by Persistent Interferometric Synthetic Aperture Radar (PS-InSAR) using Sentinel-1 SAR from 2014-2021. This study employed 250 Sentinel-1 SAR satellite images (from October 2014 to April 2021). These results show that displacement increased from May to November 2017 and then decreased for about a year and a half before increasing from May to July 2019. The seismic activity started again on May 28, 2019, and the volcano erupted on June 13, 2019. Before this period, PS-InSAR had detected a small sign of increasing displacements, especially in the northern part of Mount Agung. Furthermore, PS-InSAR provided the surface displacement results from the pre-eruption, co-eruption, and post-eruption periods. These results will be significant for understanding the active cycle of Mount Agung and for predicting future potential crisis periods.","PeriodicalId":104561,"journal":{"name":"2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128403566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Squint SAR Imaging Technique Based on Multi-Chip DSP 基于多芯片DSP的高斜视SAR成像技术

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688347

Lan Dong, Xingwei Meng, Daiyin Zhu

引用次数: 2

Ka-Band Drone SAR Flying without GPS 没有GPS的ka波段无人机SAR飞行

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688524

Weixian Liu, H. Feng, Yilong Lu

引用次数: 0

Implementation of ATI-GMTI process on Airborne and Automobile SAR data 机载和汽车SAR数据的ATI-GMTI处理实现

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688393

Sangho An, Duk‐jin Kim, Ji-hwan Hwang, Juyoung Song

引用次数: 2

Multi-Scale Capsule Network with Coordinate Attention for SAR Automatic Target Recognition 基于坐标关注的多尺度胶囊网络SAR目标自动识别

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688428

Hui Xu, F. Xu

{"title":"Multi-Scale Capsule Network with Coordinate Attention for SAR Automatic Target Recognition","authors":"Hui Xu, F. Xu","doi":"10.1109/APSAR52370.2021.9688428","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688428","url":null,"abstract":"Deep learning-based approaches have made impressive achievement in synthetic aperture radar automatic target recognition (SAR-ATR), due to its powerful ability to extract hierarchical features. However, data-driven neural network requires a large amount of labeled data, which is hardly obtained especially in SAR target recognition. Recently capsule network shows its potential to perform well with few training samples. Thus, this paper proposals a novel neural network based capsule network to achieve comparable performance with limited data. It is composed of an encode network and a decode network. we deploy multi-scale feature extractor and coordinate attention to extract robust features by multiple dilated convolution layers. Then we adopt capsule module to encode and preserve spatial information. The decoder network consisting of three fully connected layers is used to reconstruct input images and encourage capsules to learn discriminative features. The MSTAR 10-class dataset is used to evaluate and demonstrating the effectiveness of the proposed framework. Our experimental result shows that it can achieve high overall accuracy up to 99.59% using all training samples and 89.92% even if only 20% training samples are applied without data augmentation.","PeriodicalId":104561,"journal":{"name":"2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122387315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Efficient Instance Segmentation Method For High-Resolution SAR Imagery 高分辨率SAR图像的高效实例分割方法

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688454

Xiangfeng Zeng, Shunjun Wei, Jun Shi, Xiaoling Zhang

{"title":"Efficient Instance Segmentation Method For High-Resolution SAR Imagery","authors":"Xiangfeng Zeng, Shunjun Wei, Jun Shi, Xiaoling Zhang","doi":"10.1109/APSAR52370.2021.9688454","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688454","url":null,"abstract":"Benefiting from the characteristic of synthetic aperture radar (SAR), ship detection plays a crucial role in military purposes recently. Meanwhile, the methods developed by convolutional neural networks are superior in precision, detection speed, and migration ability to traditional methods. However, bounding boxes used in ship detection merely perform instance-level detection which comprises ship and background in the detected result. In terms of this defect, we introduce instance segmentation in SAR imagery for extracting the contour of the ships. Compared to ship detection with bounding boxes, instance segmentation implements pixel-level prediction for the ships, and the predicted mask eliminates background information, which provides the contour and azimuth of the ships. Furthermore, we proposed DB loss and high-resolution representations for instance segmentation. Quantitative and visualized results demonstrate proposed method improves average precision (AP) and reduces the model size compared to Mask R-CNN. Besides, it can accurately segment the hard samples, e.g., offshore ships, small ships, and ships in the port, which has a broad application vision in interpreting SAR images.","PeriodicalId":104561,"journal":{"name":"2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126533718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Sensor Satellite-Imaging Data Fusion for Earthquake Damage Assessment and the Significant Features 面向震害评估的多传感器卫星成像数据融合及其显著特征

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688438

Gunjan Joshi, R. Natsuaki, Akira Hirose

引用次数: 4

Analysis on the Impact of Constellation Configuration over Multistatic SAR Images 星座构型对多基地SAR图像的影响分析

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688406

Chaerin Chung, Sumin Kim, Jeongbae Kim, Uimin Lee, Byungdoo Lee, M. Ka

引用次数: 2

Analysis of the Spatial-variation Features of Scintillation Phase Error in Spaceborne Synthetic Aperture Radar Images 星载合成孔径雷达图像闪烁相位误差空间变化特征分析

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688351

Nan Gan, Qilei Zhang, Yongsheng Zhang, Yifei Ji, Z. Dong, Lei Yu

引用次数: 0

An Improved Signal Processing Algorithm Based on Doppler Shifting Technique for Video-SAR 基于多普勒频移技术的视频sar信号处理改进算法

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688536

C. Kim, S. Park

{"title":"An Improved Signal Processing Algorithm Based on Doppler Shifting Technique for Video-SAR","authors":"C. Kim, S. Park","doi":"10.1109/APSAR52370.2021.9688536","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688536","url":null,"abstract":"One of the technologies in synthetic aperture radar (SAR) is Video-SAR (ViSAR). Previously, we proposed a new signal processing method to generate the multi-frames of ViSAR using Doppler shifting technique via sub-aperture. In this conference, we announce an improved Doppler shifting technique using Fractional Fourier Transform (FrFT). FrFT is one of the Signal Transform methods, which is to classify each signal from the overlapped frequency band. The previous technique only uses the matched filter in the slow-time plane to compress the interested signal. However, it cannot sufficiently guarantee the interested signal in a strong interfering environment. Therefore, to increase the quality of the signals for ViSAR frames, we apply FrFT into the Doppler shifting technique. By using FrFT technique, not only the quality of the interested signal has improved, but also a function to distinguish moving targets has been added. As a result, this proposed method is more efficient than the previous one, and at the same time, it also proposes a ViSAR algorithm with high performance by adding GMTI functions. We have performed the practical experiments using X-band chirp pulse SAR system mounted on an airplane to verify the proposed method.","PeriodicalId":104561,"journal":{"name":"2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114987192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0