2012 Second International Workshop on Earth Observation and Remote Sensing Applications最新文献_第3页

Elevation change of Lambert-Amery system from ICESat/GLAS data 基于ICESat/GLAS数据的Lambert-Amery系统高程变化

2012 Second International Workshop on Earth Observation and Remote Sensing Applications Pub Date : 2012-06-08 DOI: 10.1109/EORSA.2012.6261175

Jun Liu, X. Tong, Shijie Liu, Huan Xie, H. Fan, T. Feng, Zhenxiong Gu

引用次数: 2

A novel classification method based on texture analysis using high-resolution SAR and optical data 基于高分辨率SAR和光学数据的纹理分析分类方法

2012 Second International Workshop on Earth Observation and Remote Sensing Applications Pub Date : 2012-06-08 DOI: 10.1109/EORSA.2012.6261162

Yun-xiao Luo, Shuhe Zhao, Hongkui Zhou, A. Wang, Kexun He, Lei Tan

{"title":"A novel classification method based on texture analysis using high-resolution SAR and optical data","authors":"Yun-xiao Luo, Shuhe Zhao, Hongkui Zhou, A. Wang, Kexun He, Lei Tan","doi":"10.1109/EORSA.2012.6261162","DOIUrl":"https://doi.org/10.1109/EORSA.2012.6261162","url":null,"abstract":"Data fusion technique is an efficient way to benefit multi-source, multi-platform, and multi-angle remotely sensed information. Optical imagery and SAR (synthetic aperture radar) data are complementary in terms of capability of data acquisition and image characteristics. With their different capability and their unique information content respectively, fusion of high resolution SAR and optical multi-spectral imagery can improve the classification accuracy in land use. Texture information plays an important role for class discrimination especially in SAR imagery for its backscatter is sensitive to the type, orientation, homogeneity and spatial relationship of ground objects. In order to take full advantage of multi-source remotely sensed data and combine different features of them, this paper put forward a data fusion method for high spatial resolution remotely sensed data based on texture analysis. Texture features of high resolution SAR imagery were extracted using GLCM (Grey Level Co-occurrence Matrix) method. The texture features were detected in 0°, 45°, 90° and 135° four directions, and the moving window size of 3×3, 5×5, to 31×31, 41×41, 51×51, and 61×61 were tested to analyze the influences among them. The selected texture features were added with SAR data to make classification next. Both the two imagery were classified using an object-based and rule-based approach. Then, a decision level fusion was implemented and the accuracy of classification result was improved from 78.7% and 83.0% to 88.8%.","PeriodicalId":132133,"journal":{"name":"2012 Second International Workshop on Earth Observation and Remote Sensing Applications","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131886013","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

Automated detection of coastline using Landsat TM based on water index and edge detection methods 基于水指数和边缘检测方法的Landsat TM海岸线自动检测

2012 Second International Workshop on Earth Observation and Remote Sensing Applications Pub Date : 2012-06-08 DOI: 10.1109/EORSA.2012.6261155

Xu-kai Zhang, Z. Xia, Qiong-qiong Lan, Muhammad Hasan Ali Baig

引用次数: 15

Methane in China - 2003 to 2010 from AIRS hyper spectral infrared observations 中国甲烷- 2003 - 2010年AIRS高光谱红外观测

2012 Second International Workshop on Earth Observation and Remote Sensing Applications Pub Date : 2012-06-08 DOI: 10.1109/EORSA.2012.6261140

Ying Zhang, Liangfu Chen, J. Tao, L. Su, M. Zou, M. Fan

{"title":"Methane in China - 2003 to 2010 from AIRS hyper spectral infrared observations","authors":"Ying Zhang, Liangfu Chen, J. Tao, L. Su, M. Zou, M. Fan","doi":"10.1109/EORSA.2012.6261140","DOIUrl":"https://doi.org/10.1109/EORSA.2012.6261140","url":null,"abstract":"Spaceborne measurements by Atmospheric Infrared Sounder (AIRS) on the EOS/Aqua satellite provide a global view of methane (CH4) distribution in the mid-upper troposphere (MUT-CH4) since September, 2002. In the AIRS retrieval algorithm, a modified Optimal Estimation Model (OEM) is utilized based on Singular Value Decomposition (SVD) technique, which minimizes the dependence of the solution to the first-guess field and the first-guess error characteristics and can get an accurate CH4 retrieval at the maximum sensitive layer in the mid-upper troposphere. Nevertheless, in the less sensitive layer, the CH4 retrieval is close to the first-guess. Therefore, a better first-guess can improve the accuracy and efficiency of CH4 vertical profile retrieval. In this paper, a stable inversion model based on Empirical Orthogonal Functions (EOF) of computational efficiency and acceptable accuracy was developed to solve the above problems. Based on RTTOV 10.2 (a high precision atmospheric radiative transfer model for AIRS), an empirical regression model based on orthogonal function was formulized to retrieve CH4 vertical profile from spacebonre hyperspectral infrared observations. Validation of AIRS CH4 total column amount retrieved from EOF versus FTIR measurements shows that its Person correlative coefficient is bigger than 0.85. The CH4 volume mixing ratio (VMR) near surface retrieved from EOF versus Mt. Waliguan, China (WLG, 36.2879°N 100.8964°E, 3810 m) shows that the RMS error is less than 1.5%. The regression model based on EOF can retrieve CH4 profile from spaceborne hyperspectral infrared observations in a rapid and stable way.","PeriodicalId":132133,"journal":{"name":"2012 Second International Workshop on Earth Observation and Remote Sensing Applications","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134270928","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

Estimating sea ice thickness using MODIS data: A case study in the Bohai Sea, China 利用MODIS数据估算海冰厚度:以渤海为例

2012 Second International Workshop on Earth Observation and Remote Sensing Applications Pub Date : 2012-06-08 DOI: 10.1109/EORSA.2012.6261146

Hua Su, Yunpeng Wang

{"title":"Estimating sea ice thickness using MODIS data: A case study in the Bohai Sea, China","authors":"Hua Su, Yunpeng Wang","doi":"10.1109/EORSA.2012.6261146","DOIUrl":"https://doi.org/10.1109/EORSA.2012.6261146","url":null,"abstract":"In this paper, we present a direct approach to effectively estimate sea ice thickness over a large spatial area of the Bohai Sea using EOS MODIS data. It is based on the model of an exponential relationship between albedo and thickness of sea ice. 18 images of EOS MODIS L1B data in the 2009–2010 winter were used to estimate the sea ice thickness and to monitor its spatiotemporal evolutions in the Bohai Sea. The estimated thickness results are validated by using the Lebedev and Zubov models as well as the forecasting data from the National Marine Environmental Forecasting Centre of China. Model correlation coefficients (R2=0.84−0.85) and close similarity in thickness prediction attest to the reliability and applicability of the proposed method. The average ice thickness of the whole Bohai Sea ranged from 3 to 21 cm, and the estimated maximum was about 40 cm in Liaodong Bay. Multiple-temporal maps of sea-ice thickness show that the sea ice first formed along the coastline, and gradually expanded away from the shore. Sea ice first appeared in the Liaodong Bay, and hugged the coast southwards to Bohai and Laizhou Bay. Our results also show that sea ice extent and thickness are significantly correlated with the value of θ, the difference between cumulative FDD (Freezing Degree Days) and TDD (Thawing Degree Days).","PeriodicalId":132133,"journal":{"name":"2012 Second International Workshop on Earth Observation and Remote Sensing Applications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133193889","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: 5

High voltage power line scattering feature analysis in multi SAR sensors and dual polarization 多SAR传感器和双极化下高压电力线散射特性分析

2012 Second International Workshop on Earth Observation and Remote Sensing Applications Pub Date : 2012-06-08 DOI: 10.1109/EORSA.2012.6261170

Liu Yan, Ailing Hou, Li Sha, Xingkai Li, Wenhao Wu, L. Tao

引用次数: 12

UAV-based low-altitude aerial photogrammetric application in mine areas measurement 基于无人机的低空航空摄影测量在矿区测量中的应用

2012 Second International Workshop on Earth Observation and Remote Sensing Applications Pub Date : 2012-06-08 DOI: 10.1109/EORSA.2012.6261173

Xiangfeng Liu, Peng Chen, X. Tong, Shuang Liu, Shijie Liu, Zhonghua Hong, Lingyun Li, K. Luan

引用次数: 17

Estimation of Solar Induced Chlorophyll Fluorescence from EO-1 Hyperion EO-1 Hyperion太阳诱导叶绿素荧光的估计

2012 Second International Workshop on Earth Observation and Remote Sensing Applications Pub Date : 2012-06-08 DOI: 10.1109/EORSA.2012.6261191

Shunshi Hu, Lifu Zhang, Q. Tong

引用次数: 1

Oil Slope Index: An algorithm for crude oil spill detection with imaging spectroscopy 石油斜率指数:一种基于成像光谱的原油泄漏检测算法

2012 Second International Workshop on Earth Observation and Remote Sensing Applications Pub Date : 2012-06-08 DOI: 10.1109/EORSA.2012.6261132

Qingting Li, Linlin Lu, Bing Zhang, Qingxi Tong

{"title":"Oil Slope Index: An algorithm for crude oil spill detection with imaging spectroscopy","authors":"Qingting Li, Linlin Lu, Bing Zhang, Qingxi Tong","doi":"10.1109/EORSA.2012.6261132","DOIUrl":"https://doi.org/10.1109/EORSA.2012.6261132","url":null,"abstract":"Marine oil spill is a major threat to marine and coastal ecosystems and is seen relatively often, such as the Deepwater Horizon oil spill disaster in the Gulf of Mexico in 2010 and Bohai Sea oil spills in China in 2011. Fast and accurate discrimination of oil spill is the largest challenge in detection of oil spills using remote sensing technology. In this research imaging spectroscopic analysis and Oil Slope Index(OSI) were developed to map the locations of surface crude oil in Gulf of Mexico using the SpecTIR data which was collected at 2.2m GSD and 360 spectral channels, covering 390–2450nm. The spectral features and differences of the main objects of oil, sea water and clouds can be found in the DN value of pixel spectra. The slope difference in the range from 550nm to 750nm between crude oil and other objects can be taken as a key feature for detection of crude oil on the sea surface. The Oil Slope Index(OSI) avoids the absorption bands of O2 and H2O in the air and transforms the imaging spectroscopy data into a single band image that shows the distribution of crude oil spill. OSI values can be easily calculated from radiance or DN data and no additional pre-processing of the imagery was necessary before crude oil detection. The result shows that the algorithms work well for oil spill detection which integrated the spectral feature of oil, sea water and clouds by establishing a decision tree. The automatic determination of thresholds by applying Otsu's image segmentation can realize the fast and automatic extraction of surface crude oil. This study demonstrated that the Oil Slope Index (OSI) has the potential to become a useful image processing algorithm and operational tool for imaging spectroscopy detection of crude oil spill.","PeriodicalId":132133,"journal":{"name":"2012 Second International Workshop on Earth Observation and Remote Sensing Applications","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114712736","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: 7

The monitoring of ecological reclamation evolution and landscape ecology remodeling technology in mining subsidence area 采煤沉陷区生态复垦演变监测与景观生态重塑技术

2012 Second International Workshop on Earth Observation and Remote Sensing Applications Pub Date : 2012-06-08 DOI: 10.1109/EORSA.2012.6261151

Xingli Li, Shuzhi Li, Junhai Gao

引用次数: 1