2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)最新文献_第2页

Aviation Turbulence on Indonesia and The Relationship to Himawari-8 Weather Satellite Imagery 印度尼西亚航空乱流及其与hima -8气象卫星图像的关系

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688417

M. Munandar, J. Sumantyo, M. Marfai, M. P. Hadi, A. Higuchi

引用次数: 1

Study on Three-Dimensional Fast Factorized Back Projection Using One-Dimensional Bistatic Antenna Array for Security Radar 基于一维双基地天线阵的安全雷达三维快速分解反投影研究

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688424

T. Kitamura, Satoshi Kageme, K. Suwa

引用次数: 1

Classification of Ship type using Sentinel 1 Imagery 利用Sentinel 1影像进行船型分类

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688488

Muhammad Iqbal Habibie, T. A. Pianto, H. I. Akbar

{"title":"Classification of Ship type using Sentinel 1 Imagery","authors":"Muhammad Iqbal Habibie, T. A. Pianto, H. I. Akbar","doi":"10.1109/APSAR52370.2021.9688488","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688488","url":null,"abstract":"Indonesia is an archipelagic in Southeast Asia, which located between the Indian Ocean and the Pacific Ocean. Indonesia’s sea area is 70% of its total area. Water was covered almost 72 percent of the Earth’s atmosphere. This is the most important for monitoring for maritime supervision and control. Ship identification is a very important component of marine surveillance. This surveillance can be helpful in a variety of applications, such as maritime trade, dealing illegal fishing, and oil spill monitoring. Synthetic Aperture Radar (SAR) is an active microwave remote sensing system that can identify marine features on the sea surface and penetrate clouds from space. It is used to identify marine formations on the sea surface in tropical regions. Sentinel 1 availability as a SAR image with have ability for day/night operation illumination conditions, makes it is appropriate for accurate and precise ship operations at sea. Many false alarms are triggered by the presence of land, it has been discovered. These are minimized by using land masks, which are buffered coastlines. The identification from SAR satellite is to identify the ship from the missing Automation Identification systems (AIS) and able to know the type of classification of the ship. The results will be used classification method from machine learning to identify the ship type.","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":"130479020","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

Flood Identification Model Design with Deep Learning 基于深度学习的洪水识别模型设计

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688418

Jungha Lee, Kyeongrok Kim, Jae-Hyun Kim

{"title":"Flood Identification Model Design with Deep Learning","authors":"Jungha Lee, Kyeongrok Kim, Jae-Hyun Kim","doi":"10.1109/APSAR52370.2021.9688418","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688418","url":null,"abstract":"Deep learning has been widely used in various areas, such as detecting materials, or estimating natural disasters. Especially, generative adversarial network (GAN), which is one of the deep learning models, is enhanced to CycleGAN for generation and discrimination of images even with unpaired datasets. In this paper, we design a model to generate real-like fake flood models, and we confirm that we distinguish between real and fake images by mixing them with real images. Based on this metric, a deep learning model is designed, and a dataset is generated using CycleGAN. We further perform data augmentation to assist in the dataset generation process. The program used to design the model is Python, which uses data from Sentinel-l. Input data is a collection of data from floods during 2019 in West Africa, Southeast Africa, Middle East Asia, and Australia. To determine the accuracy of the generated data, we compare the image using several indicators. The used indicators judge the accuracy and similarity of images such as SSIM and MSE, and PSNR. SSIM, MSE, and PSNR averaged 0.7192, 2014.0066, and 15.5745, respectively. Comparing images with these indicators, we confirm that the actual flood image and the generated flood image are similar. And using generated images, we use different deep learning model, to confirm how similar the real flood image is to the flood image produced in this paper.","PeriodicalId":104561,"journal":{"name":"2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"7 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":"126357806","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

Utilization of Sentinel-1 and Sentinel-2 Time-Series Data for Mapping Paddy Fields Changes in Klaten Regency, Indonesia 利用Sentinel-1和Sentinel-2时间序列数据绘制印尼Klaten县稻田变化

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688480

Giara Iman Nanda, P. Kricella, R. Shofiyati, Kustiyo

{"title":"Utilization of Sentinel-1 and Sentinel-2 Time-Series Data for Mapping Paddy Fields Changes in Klaten Regency, Indonesia","authors":"Giara Iman Nanda, P. Kricella, R. Shofiyati, Kustiyo","doi":"10.1109/APSAR52370.2021.9688480","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688480","url":null,"abstract":"Paddy fields area has a strategic function as the main food provider for the majority of Indonesia's population. Based on the fixed area of paddy fields from 2013 to 2019, there have been three significant changes in the national paddy fields area. Changes in paddy fields into other land use occurred massively, especially in urban and semi-urban areas. The utilization of remote sensing can be one of the methods in detecting changes in paddy fields area temporally. The purpose of this study was to calculate changes in the area and its distribution of paddy fields in Klaten Regency from 2016 to 2020 using multi-sensor remote sensing data. The method used in this study was detecting paddy fields area temporally in 2016, 2018, and 2020 adopting machine learning approach Random Forest classification using Sentinel-1 and Sentine1-2 imagery. The results of the analysis of paddy fields area with Random Forest method using Sentine1-2 indicated an average test accuracy value of 95% compared to Sentinel-1 analysis with an average test accuracy value of 92%. The results of the analysis of the changes in paddy fields area in Klaten Regency using Sentinel-1 showed that in 2016 there were 30,480 hectares of paddy fields area and in 2020 it decreased to 29,588 hectares. Furthermore, the results of the analysis paddy fields area using Sentine1-2 in 2016 was 31,683 hectares and decreased to 31,196 hectares in 2020. These results demonstrated an under-estimate in the calculation of the results of paddy fields area analyzed by Sentinel-1 and Sentine1-2 compared to the officially fixed area of paddy fields released by ATR/BPN.","PeriodicalId":104561,"journal":{"name":"2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"12 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":"125335235","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

Comparison of MF and CS Algorithm in 3-D Near-Field SAR Imaging 三维近场SAR成像中MF算法与CS算法的比较

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688546

Zichen Zhou, Shunjun Wei, Mou Wang, Xinyuan Liu, Jinshan Wei, Jun Shi, Xiaoling Zhang

引用次数: 3

Oil Platform Detection in Polarimetric SAR Images Based on Level Set Segmentation and Convolutional Neural Network 基于水平集分割和卷积神经网络的极化SAR图像石油平台检测

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688339

Chun Liu, Tingting Wu, Zenghui Li

引用次数: 0

Robust Adaptive Pulse Compression Under Encode-Decoder Network for Synthetic Aperture Radar 合成孔径雷达编解码器网络下的鲁棒自适应脉冲压缩

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688403

Q. Lu, Xiji Wang, Zhicheng Wang, Xianchao Ma, Na Yang

{"title":"Robust Adaptive Pulse Compression Under Encode-Decoder Network for Synthetic Aperture Radar","authors":"Q. Lu, Xiji Wang, Zhicheng Wang, Xianchao Ma, Na Yang","doi":"10.1109/APSAR52370.2021.9688403","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688403","url":null,"abstract":"The matched filter is widely used in traditional synthetic aperture radar system with the criteria of maximum output signal-noise-ratio (SNR). However, matched filter has high-level side lobes (for example, 13. 6dB without windowing) resulting in severe masking for adjacent weak targets. Recently, an adaptive pulse compression (APC) was developed based on minimum mean square error (MMSE) between true ground profiles and compressed outputs. Though has better compression results, it requires tremendous amount of time for processing, which hinders its applications. In this paper, a fast implementation named iterative adaptive pulse compression (IAPC), is designed whereby the respective tap coefficients of all filters are jointly updated with a recursive way and subsequently we incorporate it into the classic range Doppler algorithm via changing the processing sequence, which is shown to be obviously efficient with pulse duration increasing. Moreover, an encoder-decoder architecture is developed for improving the targets’ energy to solve the SNR-dependence of both APC and IAPC. As a result, through various stressing experiments including simulation and real data validation, the proposed method is shown to be superior to the original APC and matched filter in the views of time efficiency and peak sidelobe ratio (PSLR) respectively.","PeriodicalId":104561,"journal":{"name":"2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"13 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":"131471842","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

Soil moisture estimation using Simulated NISAR Dual Polarimetric GRD Product over croplands 利用模拟NISAR双偏振GRD产品估算农田土壤水分

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688350

Narayanarao Bhogapurapu, S. Dey, A. Bhattacharya, Y. S. Rao

{"title":"Soil moisture estimation using Simulated NISAR Dual Polarimetric GRD Product over croplands","authors":"Narayanarao Bhogapurapu, S. Dey, A. Bhattacharya, Y. S. Rao","doi":"10.1109/APSAR52370.2021.9688350","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688350","url":null,"abstract":"Synthetic Aperture Radar (SAR) has immense potential in estimating soil moisture with high-resolution imaging capability and cloud independent acquisition ability. Nevertheless, estimation of soil moisture under vegetation cover is a challenging task. Notably, existing literature used ancillary data sources, such as optical data, to segregate the vegetation contribution in the backscatter. In this study, we propose a new Ground Range Detected (GRD) radar vegetation index for dual-pol data, DpRVIc that overcomes the typical shortcomings (such as cloud cover, asynchronous observations and saturation effect for denser canopies) associated with different optical data derived indices. This proposed descriptor jointly utilizes the copol purity of the wave and normalized co-pol intensity parameter. We then use this index in the Water Cloud Model to estimate soil moisture over croplands. Furthermore, the performance of DpRVIc is compared with Normalized Difference Vegetation Index (NDVI) by utilizing the simulated NISAR L-band dual-pol data (VV-VH, HH-HV) over a Canadian test site. The proposed method has proven to be a potential alternative to synergetic approaches with Root Mean Square Error (RMSE) ranging from 5.6% to 6.0% with DpRVIc as a vegetation descriptor. Thus, the proposed vegetation descriptor provides new insights to quantify the vegetation using dual-pol GRD SAR data. Further, the adapted soil moisture technique has opened up a new avenue for soil moisture estimation using dual-pol GRD SAR data in the presence of vegetation.","PeriodicalId":104561,"journal":{"name":"2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"61 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":"124654105","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

Fundamental Study on Man-Made Object Classification by Degree of Polarization Using PolSAR Data of Pi-SAR2-X Pi-SAR2-X偏振sar数据偏振度分类的基础研究

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688425

Yuta Suzuki, Hiroyoshi Yamda, R. Sato

引用次数: 0