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IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium最新文献

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Efficient Insar Imaging Based on Frequency-Domain Back Projection Algorithm 基于频域反投影算法的高效Insar成像
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323628
Yue Wu, Shunjun Wei, Mou Wang, Jiadian Liang, Xiao-Ling Zhang
{"title":"Efficient Insar Imaging Based on Frequency-Domain Back Projection Algorithm","authors":"Yue Wu, Shunjun Wei, Mou Wang, Jiadian Liang, Xiao-Ling Zhang","doi":"10.1109/IGARSS39084.2020.9323628","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323628","url":null,"abstract":"High resolution imaging of interferometric synthetic aperture radar (InSAR) usually requires fine focusing and phase-preserving. Time-domain back projection (TDBP) method outperforms other conventional methods at focusing and phase-preserving, but suffer from huge computational complexity when the underlying scene is large. In this article, an efficient method exploiting by frequency-domain back projection (FDBP) is presented for high-resolution InSAR imaging. In the scheme, the coherent integration of focusing is efficient achieved by frequency-domain Fourier transform, and a delayed-distance is compensated to phase-preserving of InSAR. Simulation and experiment results demonstrates that FDBP algorithm improves the computational efficiency by three times while maintaining the similar focusing accuracy compared with the conventional TDBP method.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"233 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122393266","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
Deformation Velocity Monitoring in Kunming City using Ascending and Descending Sentinel-1A Data with SBAS-InSAR Technique 基于SBAS-InSAR技术的Sentinel-1A升降数据在昆明市变形速度监测中的应用
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324650
Shipeng Guo, Yongjie Ji, X. Tian, Wangfei Zhang, Wei Kang, Yun Li, Tingwei Zhang
{"title":"Deformation Velocity Monitoring in Kunming City using Ascending and Descending Sentinel-1A Data with SBAS-InSAR Technique","authors":"Shipeng Guo, Yongjie Ji, X. Tian, Wangfei Zhang, Wei Kang, Yun Li, Tingwei Zhang","doi":"10.1109/IGARSS39084.2020.9324650","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324650","url":null,"abstract":"With the continuous acceleration of urbanization, the construction land around Dianchi Lake has been expanded, which may result in serious surface deformation and produce adverse environmental impacts. To explore the influence of city expansion on ground subsidence in Kunming, in this paper, we used the SBAS-InSAR technique to invert the time series deformation velocity of Kunming by sentinel-1A datasets acquired from October 2018 to October 2019. The results showed that an average subsidence velocity up to-30mm/year at the north bank of Dianchi Lake. The deformation velocities extracted from descending and ascending datasets show similar results with R2 equaling to 0.9677. The results showed the potentiality of SBAS-InSAR technology and provides a more effective monitoring method for surface subsidence in mountain areas.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122521539","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
Evaluatiing the NDLI's Performance for Identifying Water Surface Using Sentinel-2 MSI Data 利用Sentinel-2 MSI数据评估NDLI识别水面的性能
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323478
K. Nguyen, Y. Liou, Le-Thu Ho
{"title":"Evaluatiing the NDLI's Performance for Identifying Water Surface Using Sentinel-2 MSI Data","authors":"K. Nguyen, Y. Liou, Le-Thu Ho","doi":"10.1109/IGARSS39084.2020.9323478","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323478","url":null,"abstract":"Land cover and land use (LULC) are the key determinant factors that influence the regional climate. In this study, we present LULC classification for the Taipei City, Taiwan based on Sentinel-2B image acquired in 2018. A recently-proposed Nomalized Difference Laten Heat Index (NDLI) and Nomalized Difference Vegetation Index (NDVI) are ultilized and compared to derive LULC, in particular, water bodies. Validation is based on reference datasets collected from Google Earth and field survey. Overall accuracices of classification are about 76% for NDLI and 91% for NDVI. However, it is shown that NDLI is highly capable to distinguish the water bodies from the others, such as built-up and bareland with accuracies of 100% and 95%, respectively, while NDVI shows better perfomance on vegetation classificantion only. In addition, it is found that shortwave infrared (SWIR)-2 (band 12) is more sensitive to identify the water bodies in comparison to SWIR-1 (band 11) of Sentinel-2B image to compute NDLI for extracting water bodies. This result further demonstrates that NDLI can be used as an effective indicator to detect and map the water surface or built-up or bareland by using Sentinel-2 imagery as initially suggested by Liou et al. [1].","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122667321","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
LAPAN'S Mid Wavelength Infrared Camera Module LAPAN的中波长红外相机模块
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323882
Bustanul Arifin, A. M. Tahir, I. Priyanto
{"title":"LAPAN'S Mid Wavelength Infrared Camera Module","authors":"Bustanul Arifin, A. M. Tahir, I. Priyanto","doi":"10.1109/IGARSS39084.2020.9323882","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323882","url":null,"abstract":"Equipped with 384 x 288 pixels VOx uncooled microbolometer, LAPAN's Mid wavelength Infrared (MWIR) camera module has been designed to detect and monitor Indonesian volcanic events and peat land fires, and their parameters. Based on the spectral range used, 3-4 µm, the MWIR camera is designed by analyzing optical parts first using commercial software, Zemax, then, developing mechanical designs by Solidwork, analyzing structures / thermal with Thermal Desktop / Sinda Fluit, and drafting of electronic design. The concept of calibration is also added in this paper. Images of all procesess indicate that LAPAN's MWIR camera design meet all our technical requirements, such as good image quality; high transmission, high durability, strong thermal stability, low cost, light weight, and small size. All these things make LAPAN's MWIR camera has dual use capabilities, space application for civilian and military.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122928120","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
A Quantitative Analysis on Different Carbonate Indicators Based on Spaceborne Data in a Controlled Karst Area 基于星载数据的岩溶控制区不同碳酸盐岩指标定量分析
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323434
Marianne Müller, V. Sales, D. Zanotta, Ademir Marques, T. T. Guimarães, L. Bachi, E. Souza, Diego Brum, L. Gonzaga, M. Veronez, C. Cazarin
{"title":"A Quantitative Analysis on Different Carbonate Indicators Based on Spaceborne Data in a Controlled Karst Area","authors":"Marianne Müller, V. Sales, D. Zanotta, Ademir Marques, T. T. Guimarães, L. Bachi, E. Souza, Diego Brum, L. Gonzaga, M. Veronez, C. Cazarin","doi":"10.1109/IGARSS39084.2020.9323434","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323434","url":null,"abstract":"New sensors aboard recently launched satellites have induced the development of several measures aimed to indicate the presence of many materials over the Earth. Karsts are places rich in carbonate rocks and present large economic and environmental importance. This paper aimed at assessing the performance and consistency of different carbonate estimators derived from orbital images acquired over a controlled karst area. Experiments were assisted by a multi-scaled reference data built through a high spatial resolution Unmanned Aerial Vehicle (UAV) image acquired over the selected area. Results show a considerable unconformity among selected measures and better performance presented by indices exploiting measures along visible and infrared spectral regions.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114055470","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
Stolt Migration Imaging for Short-Pulse Ground-Penetrating Radar Based on Compressive Sensing 基于压缩感知的短脉冲探地雷达Stolt偏移成像
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323713
L. Qu, Z. Li, A. Fathy
{"title":"Stolt Migration Imaging for Short-Pulse Ground-Penetrating Radar Based on Compressive Sensing","authors":"L. Qu, Z. Li, A. Fathy","doi":"10.1109/IGARSS39084.2020.9323713","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323713","url":null,"abstract":"An innovative compressive sensing (CS) based Stolt migration imaging algorithm for short-pulse ground-penetrating radar (GPR) has been developed and will be presented here. The traditional Stolt migration algorithm requires a wideband signal and large antenna array for implementing a high-resolution imaging reconstruction, which traditionally suffers from high sampling rate requirements and long time for data collection. On the contrary, the proposed CS-based Stolt migration imaging algorithm establishes a sparse transform between the raw measurement data and the migrated imaging results, it considers the physical propagation process of the electromagnetic wave and does not require a prior knowledge of the transmitted pulse. This imaging algorithm can provide better imaging quality; while reducing both the required sampling rate and number of measurements. The accurate imaging results from the numerical simulation data presented here verified the effectiveness and validity of the proposed imaging algorithm.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114143535","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
Improved Vegetation and Wildfire Fuel Type Mapping Using NASA AVIRIS-NG Hyperspectral Data, Interior AK 利用NASA AVIRIS-NG高光谱数据改进植被和野火燃料类型制图，Interior AK
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324136
C. Smith, S. Panda, U. Bhatt, F. Meyer, Robert W. Haan
{"title":"Improved Vegetation and Wildfire Fuel Type Mapping Using NASA AVIRIS-NG Hyperspectral Data, Interior AK","authors":"C. Smith, S. Panda, U. Bhatt, F. Meyer, Robert W. Haan","doi":"10.1109/IGARSS39084.2020.9324136","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324136","url":null,"abstract":"In Alaska, wildfire map products have traditionally been generated from lower spatial and spectral resolution Landsat imagery such as LANDFIRE Program's Existing Vegetation Type (EVT) resulting in products that do not accurately assess fire fuel types for local sites. In this study we demonstrate the efficacy of AVIRIS-NG hyperspectral data for mapping Interior Alaska's vegetation and fuel type. Based on an evaluation of field plot data collected by the project team in 2019, the new vegetation map derived from AVIRIS-NG at Viereck IV level resulted in a 73% classification accuracy compared to the 32% accuracy of the LANDFIRE's product EVT derived from Landsat 8. Not only did our product more accurately classify fire fuels, it was also able to identify 20 dominant vegetation classes (percent cover > 1%) while the EVT product only identified eight dominant classes within the study area.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"37 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114182284","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
Evaluation of the Relationship Between IASI NH3R-I Total Column and Terrestrial Vegetation Conditions IASI NH3R-I总柱与陆地植被条件关系的评价
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323107
Zihua Wu, Q. Qin
{"title":"Evaluation of the Relationship Between IASI NH3R-I Total Column and Terrestrial Vegetation Conditions","authors":"Zihua Wu, Q. Qin","doi":"10.1109/IGARSS39084.2020.9323107","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323107","url":null,"abstract":"The IASI NH3R-I total column products provide a new perspective for monitoring global atmospheric ammonia (NH3) concentration. However, there are few evaluations on its correlation with vegetation conditions. In this paper, we use binning and resampling to evaluate the relationship between IASI-retrieved NH3 concentration and MODIS-retrieved vegetation indices from 2008 to 2016. As the results show, there is a significant positive correlation between NH3 concentration and vegetation conditions. The relationship has a clear intraannual pattern, while it has great spatial heterogeneity. Another finding is that nighttime NH3 observations generally have a better correlation with vegetation conditions. Still, deeper investigations and model simulations are needed to explain the mechanism behind this relationship and its spatial and temporal patterns.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114287655","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
Creating RGB Images from Hyperspectral Images Using a Color Matching Function 使用颜色匹配功能从高光谱图像创建RGB图像
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323397
Magnus I. Magnusson, J. Sigurdsson, Sveinn Eirikur Armansson, M. Ulfarsson, H. Deborah, J. R. Sveinsson
{"title":"Creating RGB Images from Hyperspectral Images Using a Color Matching Function","authors":"Magnus I. Magnusson, J. Sigurdsson, Sveinn Eirikur Armansson, M. Ulfarsson, H. Deborah, J. R. Sveinsson","doi":"10.1109/IGARSS39084.2020.9323397","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323397","url":null,"abstract":"Hyperspectral images (HSI) are composed of hundreds of spectral bands, covering a broad range of the electromagnetic spectrum. However, images can only be visualized using three spectral channels for red, green, and blue (RGB) colors. Generating realistic RGB images using HSI is seldom the main focus of remote sensing researchers, and is therefore sometimes lacking. In this paper, we present an algorithm which creates realistic color images of HSI, using standardized methods. Research, conducted on the human perception of color in the 1920s culminated in the CIE 1931 XYZ color space. The algorithm maps every spectral band in the visible spectrum to the XYZ color space, using D65 as the reference illuminant, and then maps the XYZ to the sRGB (standard Red Green Blue) color space. The image is gamma-corrected and finally thresholded to improve contrast. The method was validated using two HSIs, creating realistic color images.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114468738","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: 21
AN EMPIRICAL STUDY ON FULLY CONVOLUTIONAL NETWORK AND HYPERCOLUMN METHODS FOR UAV REMOTE SENSING IMAGERY CLASSIFICATION 基于全卷积网络和超列方法的无人机遥感影像分类实证研究
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323562
L. Su, Yuxia Huang, Zhiyong Hu
{"title":"AN EMPIRICAL STUDY ON FULLY CONVOLUTIONAL NETWORK AND HYPERCOLUMN METHODS FOR UAV REMOTE SENSING IMAGERY CLASSIFICATION","authors":"L. Su, Yuxia Huang, Zhiyong Hu","doi":"10.1109/IGARSS39084.2020.9323562","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323562","url":null,"abstract":"Fully Convolutional Network (FCN), which can adopt various Convolutional Neural Networks (CNN), are now increasingly being used in remote sensing communities. CNN are improved constantly either in accuracy or by reducing parameters for a given equivalent accuracy. This paper investigates five widely used CNNs (AlexNet, VGG16, ResNet, SqueezeNet, and a pruned VGG16) in the context of FCN for coastal beach classification of imagery acquired by Unmanned Aerial Vehicles (UAV). Our experiments show that (1) not every CNN is suitable to FCN for semantic segmentations of images though each CNN approximately achieved an equivalent accuracy for image labeling; (2) band reduced pruning of existing CNN has the least impact on implementation and accuracy. To examine the capability of convolutional layers capturing semantic features, this paper also carries out beach classification experiments using hypercolumn methods with VGG16.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114519551","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
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