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• 地理科学与资源研究所 [2]
• 新疆生态与地理研究所 [2]
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• OAI收割 [11]

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筛选

浏览/检索结果: 共11条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Cloud Detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1D Convolutional Neural Network 期刊论文  OAI收割 REMOTE SENSING, 2021, 卷号: 13, 期号: 16, 页码: 20 作者:  Ma, Nan;  Sun, Lin;  Zhou, Chenghu;  He, Yawen   |  收藏  |  浏览/下载：37/0  |  提交时间：2021/11/05 ASTER spectral library  hyperspectral data  1D convolutional neural network  cloud detection  data simulation  multi-satellite remote sensing images   Using multi-angle hyperspectral data to monitor canopy leaf nitrogen content of wheat SCI/SSCI论文  OAI收割 2016 作者:  Song X.;  Xu, D. Y.;  He, L.;  Feng, W.;  Wang, Y. H.   |  收藏  |  浏览/下载：37/0  |  提交时间：2017/11/09 Multi-angle reflectance  Hyperspectral  Leaf nitrogen content  Wheat  Model  remote-sensing data  spectral reflectance  vegetation indexes  model  rice  crop   Prediction of soil properties using laboratory VIS-NIR spectroscopy and Hyperion imagery 期刊论文  OAI收割 JOURNAL OF GEOCHEMICAL EXPLORATION, 2013, 卷号: 132, 页码: 26-33 作者:  Lu, Peng;  Wang, Li;  Niu, Zheng;  Li, Linghao;  Zhang, Wenhao   |  收藏  |  浏览/下载：4/0  |  提交时间：2023/04/25 Geostatistical methods  Spatial variability  Visible and near infrared reflectance spectroscopy  Multi-spectral sensors  Hyperspectral remote sensing   A New Data Transformation Method for Cbers-02b Multi-spectral Images 会议论文  OAI收割 Bangkok, THAILAND, September 30, 2011 - October 1, 2011 Zhang, Chengwen; Tang, Jiakui; Mi, Sujuan; Zhao, Lijun; Li, Yongzhi; Yu, Xinju   |  收藏  |  浏览/下载：29/0  |  提交时间：2013/03/11 Remote Sensing  Lbv Transformation  Multi-spectral Images  Classification  Cbers-02b   Destriping method using lifting wavelet transform of remote sensing image (EI CONFERENCE) 会议论文  OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China 作者:  He B. 收藏  |  浏览/下载：30/0  |  提交时间：2013/03/25 Based on the characteristic of striping noise in remote sensing images  a new destriping noise technique for the improved multi-threshold method using lifting wavelet transform applied to remote sensing imagery is presented in this letter. Have used the lifting wavelet decomposition algorithm  the thresholds are determined by corresponding wavelet coefficients in every scale. Remote sensing imagery is so large that the algorithm must be fast and effective. The lifting wavelet transform is easily realized and inexpensive in computer time and storage space compared with the traditional wavelet transform. We also compare the method with some traditional destriping methods both by visual inspection and by appropriate indexes of quality of the denoised images. From the comparison we can see that the adaptive threshold method can preserve the spectral characteristic of the images while effectively remove striping noise and it did better than the existed ones. 2010 IEEE.   Improvement of urban land use and land cover classification approach in arid areas 会议论文  OAI收割 Proceedings of SPIE - The International Society for Optical Engineering, Image and Signal Processing for Remote Sensing XVI, Toulouse, France, 2010 Qian; Jing1; 2; Zhou; Qiming1; Chen; Xi2 收藏  |  浏览/下载：34/0  |  提交时间：2011/08/23 Arid regions - Image analysis - Image classification - Land use - Landforms - Maximum likelihood - Photography - Pixels - Remote sensing - Signal processing - Aerial Photographs - Arid area - Bare soils - Beijing-1 - Built-up areas - Classification approach - Classification results - Construction materials - Data sets - Discrete elements - Extraction accuracy - Field investigation - High resolution image - Land-cover types - Landsat ETM - Maximum likelihood classifications - Mixed pixel - Multi-sensor data - Object oriented - object-oriented classification method - Object-oriented processing - Spectral characteristics - Stony deserts - Urban areas - Urban change detection - Urban environments - Urban land use   Using multi-spectral remote sensing data to extract and analyze the vegetation information in desert areas - A case in the western Gurbantunggut desert 会议论文  OAI收割 Proceedings-2009 International Conference on Environmental Science and Information Application Technology, ESIAT, Wuhan, China, 2009 Zhao; Huai-Bao1; Liu; Tong2; Cui; Yao-Ping3; Lei; Jia-Qiang1 收藏  |  浏览/下载：27/0  |  提交时间：2011/08/23 Correlation coefficient - Desert area - Desertification monitoring - Gurbantunggut desert - Landsat TM images - Linear relationships - Multi-spectral - NDVI - Remote sensing data - Remote sensing images - Spatial distribution - Spectral mixture analysis - TM image - Vegetation coverage - Vegetation fractions - Vegetation index   A Regional-memory-pattern Artificial Idiotypic Network and its Application in Multi-spectral Remote Sensing Image Classification 会议论文  OAI收割 2008 International Workshop on Education Technology and Training and 2008 International Workshop on Geoscience and Remote Sensing, Vol 2, Proceedings, Los Alamitos Liu Qingjie; Lin Qizhong; Wu Yunzhao; Wang Qinjun 收藏  |  浏览/下载：42/0  |  提交时间：2014/12/07 multi-spectral remote sensing  data classification  artificial immune  idiotypic network  regional-memory pattern  IMMUNE-SYSTEM   Study on shallow groundwater information extraction technology based on multi-spectral data and spatial data 期刊论文  OAI收割 SCIENCE IN CHINA SERIES E-TECHNOLOGICAL SCIENCES, 2009, 卷号: 52, 期号: 5, 页码: 1420-1428 作者:  Yu DeHao;  Deng ZhengDong;  Long Fan;  Guan HongJun;  Wang DaQing   |  收藏  |  浏览/下载：15/0  |  提交时间：2021/02/26 remote sensing  shallow groundwater  forecasting model  multi-spectral data  spatial data   A novel airborne digital camera system - art. no. 620007 会议论文  OAI收割 Remote Sensing of the Environment: 15th National Symposium on Remote Sensing of China, Bellingham Fang, Junyong; Liu, Xue; Wei, Zheng; Zhang, Bing; Zheng, Lanfen; Tong, Qingxi 收藏  |  浏览/下载：29/0  |  提交时间：2014/12/07 airborne remote sensing  area array CCD  digital camera  multi-mode  wide field  multi-spectral  stereo imaging  POS  aerial experiments