2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)最新文献_第4页

Despeckling of Synthetic Aperture Radar Image using Deep-Learning Model 基于深度学习模型的合成孔径雷达图像去斑

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688432

YuFan Cai, J. Sumantyo

{"title":"Despeckling of Synthetic Aperture Radar Image using Deep-Learning Model","authors":"YuFan Cai, J. Sumantyo","doi":"10.1109/APSAR52370.2021.9688432","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688432","url":null,"abstract":"This paper mainly studies despeckling method for Synthetic Aperture Radar (SAR) image. The denoising method based on deep learning usually needs a lot of samples and noiseless reference images to realize data regression. However, speckle noise is very different from random additive noise such as white Gaussian noise in that its distribution is affected by the background target and it is hard to obtain completely noiseless reference images. Therefore, we proposed a local learning method and constructed a deep learning model to overcome this challenge, which named Speckle-Denoise-Net (SD-Net). In proposed method, the speckle noise can be effectively suppressed without the need for noiseless reference images. It can not only solve the problem of lacking training samples and having excessive image size, but also take advantage of unsupervised learning for inherent noise to make a good balance between smoothing and structure-preserving compared With other methods. At present, the test results of this method (Including denoising effect, original information retention, and algorithm speed) have exceeded most of the existing traditional denoising algorithms and deep learning methods, which allow it to bring new inspirations to solve the problem of speckle noise.","PeriodicalId":104561,"journal":{"name":"2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"15 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":"128229300","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

Convolutional Neural Networks for Tomographic MIMO Ground Penetrating Radar Imaging 层析MIMO探地雷达成像的卷积神经网络

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688476

M. Ambrosanio, S. Franceschini, M. M. Autorino, V. Pascazio

{"title":"Convolutional Neural Networks for Tomographic MIMO Ground Penetrating Radar Imaging","authors":"M. Ambrosanio, S. Franceschini, M. M. Autorino, V. Pascazio","doi":"10.1109/APSAR52370.2021.9688476","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688476","url":null,"abstract":"Ground penetrating radar (GPR) represents a remote sensing modality which has been extensively exploited for the detection and characterisation of buried objects in a non-destructive way. To this aim, several algorithms have been developed to efficiently and automatically identify underground targets of interest. In this framework, approaches based on deep learning and convolutional neural networks (CNNs) have been proposed in the past years and recently gained a lot of attention by the scientific community. Despite their efficiency, these approaches require a large number of cases to perform the training step and improve their classification performance. In this paper, the use of multistatic GPR data is explored (via simplified numerical simulations) to automatically classify the kind of underground utility in areas in which both water and natural gas pipes can be considered. More in detail, some discussions on the classification performance obtained via considering more receivers in the measurement configuration close the paper, underlining the better results obtained via exploiting the multistatic data.","PeriodicalId":104561,"journal":{"name":"2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"518 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":"133976468","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: 1

Novel Clustering Technique for Monitoring Crop Phenology Using SAR Data 基于SAR数据的作物物候监测聚类新技术

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688426

S. Dey, Narayanarao Bhogapurapu, A. Bharracharya, D. Mandal, H. Mcnairn, Y. S. Rao

{"title":"Novel Clustering Technique for Monitoring Crop Phenology Using SAR Data","authors":"S. Dey, Narayanarao Bhogapurapu, A. Bharracharya, D. Mandal, H. Mcnairn, Y. S. Rao","doi":"10.1109/APSAR52370.2021.9688426","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688426","url":null,"abstract":"Unsupervised clustering is essential when in-hand information about crop phenological stages are not available. In addition, this technique assists in discriminating crop growth stages concerning the changes in the scattering mechanisms. Newly proposed parameter $theta_{FP}$ for full polarimetric Synthetic Aperture Radar (SAR) data is shown to be enhancing the target characterization capability over the existing parameter such as $overline{alpha}$. Following this, the existing clustering technique by combinedly utilizing the Barakat degree of polarization and elements of the Kennaugh matrix along with scattering entropy HFP is effective over land cover classes. The clustering scheme is consisting of 12 zones where zones (Zl, Z2, Z3), (Z10, Zll, Z12), and (Z4, Z5, Z6, Z7, Z8, Z9) represent even, odd and multiple scattering types, respectively. In this study, we use the RADARSAT-2 FP SAR data from Jun to Nov 2019 acquired over the Indian test site of Vijayawada under the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative. The changes and shifts of the clusters are exciting according to the phenological development of rice fields. At the initial stage, dense clusters are evident in Z10, Zll, and Z12, while, during the advanced tillering stage, more data points are found in the Z3 zone. Following this, clusters in zone Z5, Z6, Z8 and Z9 are evident during the maturity stage.","PeriodicalId":104561,"journal":{"name":"2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"233 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":"134006349","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

Long Term Ground Deformation of Mount Raung as Inferred by InSAR and GPS Data 由InSAR和GPS资料推断的朗山长期地面变形

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688412

E. Kriswati, Agustan, M. Frederik, A. Saepuloh, S. Darmawan, Hilma Alfianti

{"title":"Long Term Ground Deformation of Mount Raung as Inferred by InSAR and GPS Data","authors":"E. Kriswati, Agustan, M. Frederik, A. Saepuloh, S. Darmawan, Hilma Alfianti","doi":"10.1109/APSAR52370.2021.9688412","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688412","url":null,"abstract":"The eruptions of Mount Raung, located in East Java, Indonesia was characterized as explosive and effusive. The majority of eruptions that occurred before 1956 produced ash high into the air, and since 1921 the majority of eruptions have been lava flows to the base of the caldera. The most recent eruption occurred on January 21, 2021 where the ash column was observed to reach 400 - 1000 meters above the peak; on February 8 - 12 it even reached an altitude of 2000 - 2500 meters. Monitoring of lava (hotspot) has been carried out since February 6, 2021, which proceeded to cover almost to the entire caldera base. This eruption is probably a series of lava eruptions for the period November 2014 - August 2015 and July-August 2020. Currently, the main volcanic activity of Raung is located at the base of the elliptical caldera, measuring 1750 x 2250 m at a depth of 400-550 m below the embankment. The deformation activity of Mount Raung was observed by combining interferometric method and GNSS observation. Around 268 unwrapped interferograms since 2015 until January 2021 were provided by LiCSAR and processed by LiCSBAS method. The deformations are clearly shown on crater with deflation trend, whereas inflation trend is detected on the caldera. In short period, i.e. June to January 2021, inflation and deflation are detected on the crater. For terrestrial method, GNSS data from 3 stations that observed since December 2018 using the Leica SR30 receiver and processed by GIPSYX software. The data showed indication of deflation in the body of Mount Raung from December 2018 to mid-January 2021. Moreover, between mid-January to late February 2021 there was an indication of inflation in the peak areas. The analysis using shorter period should be able to observe the dynamics changes in the deformation of Mount Raung, but the long-term ones can determine the pressure accumulation process at the depth and the possibility of future eruption.","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":"131096094","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

Pre-assessment of the Potential of Dual Polarization of Sentinel 1 Data for Mapping the Mangrove Tree Species Distribution in South Bali, Indonesia 哨兵1号数据双极化潜力对印尼巴厘岛南部红树林树种分布的预评估

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688441

M. F. Ghazali, K. Wikantika

{"title":"Pre-assessment of the Potential of Dual Polarization of Sentinel 1 Data for Mapping the Mangrove Tree Species Distribution in South Bali, Indonesia","authors":"M. F. Ghazali, K. Wikantika","doi":"10.1109/APSAR52370.2021.9688441","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688441","url":null,"abstract":"Behind the needs of the availability for the mangrove trees biodiversity database are important in promoting coastal area protection, including mangrove forest conservation. This initiative started by mapping mangrove trees distribution by utilizing two different modes of ground detected ranged (GRD) and single look complex (SLC) of Sentinel-l data images. Both are processed to get the backscatter value, dual-polarization radar vegetation index (DpRVI) and polarization decomposition for entropy, alpha, and anisotropy values for each mangrove trees species (MTS) detection that compared with the data field. Besides that, the distribution of each mangrove tree has been conducted based on its genus and species richness that estimated using a random forest (RF) algorithm for nine different combinations, including VV, VH, and VV-VH, entropy, alpha, and anisotropy, DpRVI, and the integration of between backscatter value and polarization decomposition. The result has shown a variation in the level of accuracy ranging from weak to moderate at about 26% to 65%. The combination of VV, VH, VH-VV and DpRVI obtained better accuracy at about 65%. Related to the species of mangrove detected, the Avicennia alba becomes a dominant mangrove tree species grown as much as 60% in the study area.","PeriodicalId":104561,"journal":{"name":"2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"5 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":"134318043","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: 1

Analysis of the recovery activity after the Kumamoto Earthquake using ALOS-2/PALSAR-2 熊本地震后恢复活动的ALOS-2/PALSAR-2分析

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688517

T. Nonaka, T. Asaka, K. Iwashita

引用次数: 0

Development and Validation of Compact All Time Imaging SAR Sensor System (CATIS) 紧凑型全天候成像SAR传感器系统(CATIS)的研制与验证

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688364

Song Xue, M. Waqar, Myeong-Ryong Nam

引用次数: 0

MetaSensing Airborne InSAR Systems for DEM Generation: Results from X, C, P-Band Campaigns 用于DEM生成的超传感机载InSAR系统:来自X、C、p波段运动的结果

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688496

K. D. de Macedo, A. Coccia, A. Meta, S. Placidi

引用次数: 0

Sentinel 1 SAR for Rapid Identification of Flooded Inquiries in South Kalimantan Province Sentinel 1 SAR用于快速识别南加里曼丹省洪水查询

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688487

N. Setyaningrum, Dynosius Bryan Sencaki, F. Marpaung, Muhamad Dayuf, A. P. Perdana

{"title":"Sentinel 1 SAR for Rapid Identification of Flooded Inquiries in South Kalimantan Province","authors":"N. Setyaningrum, Dynosius Bryan Sencaki, F. Marpaung, Muhamad Dayuf, A. P. Perdana","doi":"10.1109/APSAR52370.2021.9688487","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688487","url":null,"abstract":"An 2021-flood occurred in South Kalimantan Province caused severe moral and material losses, the destruction of nature, and weakened the wheel of the economy. Rapid identification of affected flood inundation areas spatially is essential for monitoring and assisting in the preparation of emergency responses. Spatial analysis of flood areas utilizes SAR (Synthetic Aperture Radar) images because its wavelength is able to penetrate well through clouds or hazy atmosphere. This study utilizes Sentinel-1A level GRD (Ground Range Detect) - IW (Interferometric Wide Swath Mode) images with VV and VH polarization. We used Remote Sensing Change Detection (RDCD) method and then implemented a flood threshold. Flood event verification is obtained from the crowdsourcing report of flood events through social media. Results shows that 11 districts of South Kalimantan were affected by flooding, namely Balangan Regency (3,498 ha), Banjar (47,548 ha), Barito Kuala (37,530 ha), Hulu Sungai Selatan (9,939 ha), Hulu Sungai Tengah (9,789 ha), Hulu Sungai Utara (13,002.24 ha), Tabalong (4,876 ha), Tanah Laut (25,931 ha), Tapin (15,597 ha), Banjarbaru (2,812 ha) and Banjarmasin (6,570 ha).","PeriodicalId":104561,"journal":{"name":"2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"6 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":"122277587","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

Chandrayaan-2 Dual Frequency Synthetic Aperture Radar (DFSAR) Full and Compact Polarimetric Data Analysis for the Lunar Surface 月船2号双频合成孔径雷达(DFSAR)月球表面全紧凑极化数据分析

2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) Pub Date : 2021-11-01 DOI: 10.1109/APSAR52370.2021.9688528

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

{"title":"Chandrayaan-2 Dual Frequency Synthetic Aperture Radar (DFSAR) Full and Compact Polarimetric Data Analysis for the Lunar Surface","authors":"Narayanarao Bhogapurapu, A. Bhattacharya, Y. S. Rao","doi":"10.1109/APSAR52370.2021.9688528","DOIUrl":"https://doi.org/10.1109/APSAR52370.2021.9688528","url":null,"abstract":"India launched the Chandrayaan-2 satellite on 22 July 2019, with several sensors onboard to map the Lunar surface and subsurface. Among the several sensors, the Dual Frequency Synthetic Aperture Radar (DFSAR) operates in L- and S-band frequencies in full and compact polarimetric modes. This paper analyzes full- and compact polarimetric SAR data acquired by L- and S-bands to explore backscattering characteristics of lunar surface and subsurface. The data utilized in this study are collected near the south polar region of the moon. We applied a few polarimetric decomposition techniques and scattering signatures to analyze the data. First, we analyzed the model-free four-component decomposition method and the model-based Yamaguchi four-component decomposition powers over various lunar surface features for the full-pol data. Similarly, for the compact-pol data, the model-free three-component decomposition and the m – χ are employed. Second, we carried out a comparative analysis of the decomposed powers at different sensor frequencies. In this preliminary study, we observed that the recently developed model-free decomposition techniques provide meaningful insights in interpreting physical scattering mechanisms in unknown/under-explored terrains of the lunar surface.","PeriodicalId":104561,"journal":{"name":"2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"333 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":"124688800","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