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Landsat-Based Reconstruction of Corn and Soybean Yield Histories in the United States Since 1999 1999年以来美国玉米和大豆产量历史的陆地卫星重建
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323792
D. Lobell, Walter T. Dado, J. Deines, S. D. Tommaso, Sherrie Wang
{"title":"Landsat-Based Reconstruction of Corn and Soybean Yield Histories in the United States Since 1999","authors":"D. Lobell, Walter T. Dado, J. Deines, S. D. Tommaso, Sherrie Wang","doi":"10.1109/IGARSS39084.2020.9323792","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323792","url":null,"abstract":"The open Landsat archive provides a consistent view of the Earth's surface for much longer than most currently available agricultural datasets throughout the world. Here we present a summary of recent work to extend pixel-level (30 m) maps of corn and soy areas and yields back to 1999 across the entire Corn Belt of the United States. We find consistent performance back in time, as judged by comparison with county level statistics. The approaches presented here can be readily extended to other regions and to incorporate other sensors.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"160 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":"121261014","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
Interpolation of Geochemical Data with Aster Images Based on AlexNet Convolution Neural Network 基于AlexNet卷积神经网络的Aster图像地球化学数据插值
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324116
Shi Bai, Jie Zhao
{"title":"Interpolation of Geochemical Data with Aster Images Based on AlexNet Convolution Neural Network","authors":"Shi Bai, Jie Zhao","doi":"10.1109/IGARSS39084.2020.9324116","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324116","url":null,"abstract":"Being an important geological information source, geochemical data is widely used in mineral exploration, environmental protection, pollution monitoring, etc. However, geochemical data with extensive coverage and fine resolution has become inaccessible, especially in some unreachable and remote areas. Remote sensing data with fast and efficient ability to collect geology related geoinformation has long been employed in many of geological studies. Joint utilization of geochemical and remote sensing data, as well as other sources of geo-data can consequently assist in geological applications such as mineral exploration In recent decades, methodology to integrate remote sensing and geochemical data have significantly improved. During the integration, geochemical data are often interpolated or resampled to finer resolution for match that of remote sensing images but without notable improvement in geo-information quality containedwith. This study proposeda new integration method that uses the AlexNet convolution neural network to interpolate geochemical data with ASTER images. The interpolated geochemical data presents not only with a higher spatial resolution, but also with geological information from remote sensing images.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"13 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":"114070917","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
Assessing Land Suitability for Managing Urban Growth: An Application Of GIS and RS 城市发展管理中的土地适宜性评价:GIS和RS的应用
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324098
B. ShahPooja, P. Kaushik, Jaldeep Patel, C. Patel, R. Tailor
{"title":"Assessing Land Suitability for Managing Urban Growth: An Application Of GIS and RS","authors":"B. ShahPooja, P. Kaushik, Jaldeep Patel, C. Patel, R. Tailor","doi":"10.1109/IGARSS39084.2020.9324098","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324098","url":null,"abstract":"World is marching towards sustainable urban settlement and resource utilization has to be augmented. Land as a limited resource, needs to be utilized judiciously. Built density and urban fabric poses challenge to urban planner and decision maker, to plan the city in such a way that it will use land resource optimally. So this study aims to identify suitable land which has high potential to satisfy future urban needs of Surat regions a city in western part of India. Multispectral Image (LISS IV) is used to extract the Land cover map of city. From DEM, slope and elevation maps are generated. Land suitability analysis is carried out using weighted overlay tool of Geographic Information System (GIS) and weights are obtained by Analytical Hierarchy process(AHP) considering socio-economic, utilities, environment and physical as main criteria. Weightage has been given by experts in planning area and GIS environment. The result of this study shows that 82.93 sq.km (8.56%) area is very suitable, 128.84 sq.km (13.30%) is suitable, 749.82 sq.km (77.37%) is moderately suitable, 6.43 sq.km (0.66%) is less suitable and 1.07 sq.km (0.11%) is unsuitable for the future development. This study of land suitability can help the urban planner to regularize the proper zoning and to prepare the development plan as well as local plan. Also policy maker can use this map to form appropriate policy to cop up with blighted situation in urban area and to carry out sustainable planned development to achieve Sustainable Development Goals(SDGs) announced by ministry of Urban Development.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"42 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114105204","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: 4
Hyperspectral and Multispectral Image Fusion Using Non-Convex Relaxation Low Rank and Total Variation Regularization 基于非凸松弛、低秩和全变分正则化的高光谱和多光谱图像融合
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323227
Yue Yuan, Qi Wang, Xuelong Li
{"title":"Hyperspectral and Multispectral Image Fusion Using Non-Convex Relaxation Low Rank and Total Variation Regularization","authors":"Yue Yuan, Qi Wang, Xuelong Li","doi":"10.1109/IGARSS39084.2020.9323227","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323227","url":null,"abstract":"Hyperspectral (HS) and multispectral (MS) image fusion is an important task to construct an HS image with high spatial and spectral resolutions. In this paper, we present a novel HS and MS fusion method using non-convex low rank tensor approximation and total variation regularization. In specific, the Laplace based low-rank model is formed to exploit spatial-spectral correlation and nonlocal similarity of the HS image, and the second-order total variation is used to describe the local smoothness structure in the spatial domain and adjacent bands. Also, an effective optimization algorithm is designed for the proposed model. In the experiments, we demonstrate the superiority of the proposed method compared to several state-of-the-art approaches.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"34 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":"114337395","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: 4
Deep Manifold Learning Network for Hyperspectral Image Classification 用于高光谱图像分类的深度流形学习网络
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323132
Zhengying Li, Hong Huang, Chunyu Pu
{"title":"Deep Manifold Learning Network for Hyperspectral Image Classification","authors":"Zhengying Li, Hong Huang, Chunyu Pu","doi":"10.1109/IGARSS39084.2020.9323132","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323132","url":null,"abstract":"Deep neural networks have achieved great success in the field of image processing. The feature representation of RGB image can be easily obtained in spatial domain. Different from this, hyperspectral image (HSI) is a kind of high-dimensional data that contains rich spectral information. To explore the manifold structure in HSI, a new deep learning model termed deep manifold learning network (DMLN) was proposed in this paper. In DMLN, a graph based loss function is designed to combine the exploration of manifold structure and the extraction of deep abstract information, which can obtain the discriminant features by iteratively enhancing the compactness of intraclass samples and the separation of interclass samples. Experimental results on two real-world HSI data sets demonstrate the proposed DMLN outperformed some the state-of-the-art methods.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"36 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":"116305868","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
Ship Detection and Fine-Grained Recognition in Large-Format Remote Sensing Images Based on Convolutional Neural Network 基于卷积神经网络的大幅面遥感图像船舶检测与细粒度识别
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323246
Jingrun Li, J. Tian, Peng Gao, Linfeng Li
{"title":"Ship Detection and Fine-Grained Recognition in Large-Format Remote Sensing Images Based on Convolutional Neural Network","authors":"Jingrun Li, J. Tian, Peng Gao, Linfeng Li","doi":"10.1109/IGARSS39084.2020.9323246","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323246","url":null,"abstract":"Ship detection and fine-grained recognition in large-format remote sensing image are an important research direction in the field of remote sensing image detection. But less research has been done in this area. Aiming at this problem, this paper constructs a large-format remote sensing image ship target dataset with ship category information, and proposes a background filtering network and a ship fine-grained classification network. The background filtering network is used to quickly filter out the background area, and the ship fine-grained classification network is used to detect ship targets and distinguish ship categories. Compared with the previous method, the method proposed in this paper can significantly improve the efficiency of ship target detection in large-format remote sensing images, while also improving the detection accuracy.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"2 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":"116455121","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: 8
Sentinel-1/2 Time Series for Selective Logging Monitoring in Temperate Forests 基于Sentinel-1/2时间序列的温带森林选择性采伐监测
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323952
M. Tanase, Ignacio Borlaf, Ionuț-Silviu Pascu, Diana Pitar, B. Apostol, Marius Petrila, S. Chivulescu, Ș. Leca, Daniel Pitar, Albert Ciceu, A. Dobre, F. Popescu, O. Badea, C. Aponte
{"title":"Sentinel-1/2 Time Series for Selective Logging Monitoring in Temperate Forests","authors":"M. Tanase, Ignacio Borlaf, Ionuț-Silviu Pascu, Diana Pitar, B. Apostol, Marius Petrila, S. Chivulescu, Ș. Leca, Daniel Pitar, Albert Ciceu, A. Dobre, F. Popescu, O. Badea, C. Aponte","doi":"10.1109/IGARSS39084.2020.9323952","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323952","url":null,"abstract":"The aim of this study was to evaluate the utility of Sentinel-1/2 time-series for monitoring selective logging in temperate forests. Ten stands were selectively logged with 5 to 28% of the existing growing stock volume being extracted. The analysis was focused on backscatter coefficient and surface reflectance changes for dates immediately prior and past the logging period. Monthly information on leaf area index (from terrestrial laser scanning) and vegetation water content (from destructive sampling) was used to support the analysis. The analysis suggested that monitoring selective logging using Sentinel-1/2 imagery is challenging in temperate montane forests due to a range of factors, including logging time and duration, saturation of C-band wavelength, and relatively small changes in canopy cover that cannot be reliably picked up by the optical sensor.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"68 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":"121536607","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
A Neural Network Approach to Classify Mixed Classes Using Multi Frequency Sar data 基于多频Sar数据的混合分类神经网络方法
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324541
A. Kukunuri, D. Murugan, Dharmendra Singh
{"title":"A Neural Network Approach to Classify Mixed Classes Using Multi Frequency Sar data","authors":"A. Kukunuri, D. Murugan, Dharmendra Singh","doi":"10.1109/IGARSS39084.2020.9324541","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324541","url":null,"abstract":"Classification of mixed classes that are having similar backscatter response at different polarization combinations, using single frequency synthetic aperture radar (SAR) data is very intricate and there is always a high possibility of misclassification. Therefore, the main objective of this study is to classify the mixed classes using multi-frequency SAR data. An artificial neural network (ANN) approach is used for classification of the considered mixed classes using various polarimetric parameters obtained from single acquisition ALOS2 PALSAR (L band) and Sentinel 1 (C band) dual pol SAR data. An image statistical measure based separability index analysis is used to identify the optimal polarimetric parameters for developing the classifier. It is observed that, the proposed multi-frequency approach is able to classify the mixed classes with an overall accuracy of 87%.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"2015 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":"121564503","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
MONITORING ICE COVERING LAKE SAROMA BY USING SENTINEL-1 C-BAND SAR DATA 利用sentinel-1 c波段SAR数据监测冰面湖泊肉瘤
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323543
H. Wakabayashi, H. Tonooka
{"title":"MONITORING ICE COVERING LAKE SAROMA BY USING SENTINEL-1 C-BAND SAR DATA","authors":"H. Wakabayashi, H. Tonooka","doi":"10.1109/IGARSS39084.2020.9323543","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323543","url":null,"abstract":"The objective of this study is to investigate the backscattering characteristics of ice covering Lake Saroma observed by Sentinel-1 C-band SAR data. In-situ observation measuring snow depth and ice thickness was conducted at more than 100 sampling points on Lake Saroma in February 2019. We also tried to apply interferometric SAR data analysis for the data pair acquired during the period from Feb.11 to Mar.7. The C-band SAR backscattering coefficients in both VV and VH have the maximum values at 30 to 35 cm of the ice thickness, and the relatively significant correlation found in the east part of the lake. We also find that the phase difference presumably caused by a tidal change in several areas surrounded the lake. In the relatively thick ice area, the coherence decreases with ice thickness, which is caused by the dominant scattering mechanism change.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"1 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":"121634298","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
A High Resolution SAR Ship Sample Database and Ship Type Classification 高分辨率SAR船舶样本数据库与船型分类
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323826
M. Bao, J. Meng, Zhang Xi, Genwang Liu
{"title":"A High Resolution SAR Ship Sample Database and Ship Type Classification","authors":"M. Bao, J. Meng, Zhang Xi, Genwang Liu","doi":"10.1109/IGARSS39084.2020.9323826","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323826","url":null,"abstract":"As the improving of the synthetic aperture radar (SAR) resolution and the increase in the amount of data acquisition, the ship type recognition has become an important research topic. In order to meet the precise identification for ship types, 101 SAR data and the Automatic Identification System (AIS) were used to build a SAR ship database. The database contains 5288 ship samples with different polarizations, incidence angle and resolutions, including more than 20 kinds of ship type such as cargo, container, oil tankers, and fishing boats. Furthermore, the influence of different polarization, incidence angle and heading on ship geometry parameters was analyzed. Moreover, a random forest (RF) classifier was used to carry out the ship type recognition experiment, and the classification accuracy reached more than 60%.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"56 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":"121676449","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: 3
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