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检索条件"机构=Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems"
527 条 记 录,以下是291-300 订阅
排序:
SEMI-SUPERVISED OBJECT DETECTION IN REMOTE SENSING IMAGES USING GENERATIVE ADVERSARIAL NETWORKS
SEMI-SUPERVISED OBJECT DETECTION IN REMOTE SENSING IMAGES US...
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IEEE International geoscience and Remote Sensing Symposium
作者: Guowei Chen Lei Liu Wenlong Hu Zongxu Pan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China
Object detection is a challenging task in computer vision. Now many detection networks can get a good detection result when applying large training dataset. However, annotating sufficient amount of data for training i... 详细信息
来源: 评论
SUPER-RESOLUTION OF REMOTE SENSING IMAGES BASED ON TRANSFERRED GENERATIVE ADVERSARIAL NETWORK
SUPER-RESOLUTION OF REMOTE SENSING IMAGES BASED ON TRANSFERR...
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IEEE International geoscience and Remote Sensing Symposium
作者: Wen Ma Zongxu Pan Jiayi Guo Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China
Single image super-resolution (SR) has been widely studied in recent years as a crucial technique for remote sensing applications. This paper proposes a SR method for remote sensing images based on a transferred gener... 详细信息
来源: 评论
SHAPE SIMILARITY MEASURE METHOD BASED ON PRINCIPAL CURVATURE ENHANCEMENT DISTANCE TRANSFORMATION
SHAPE SIMILARITY MEASURE METHOD BASED ON PRINCIPAL CURVATURE...
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IEEE International geoscience and Remote Sensing Symposium
作者: Feng Wang Yuming Xiang Xinghui Yao Jiayin Liu Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China
The conventional shape similarity measurements of remote sensing data face problems in the situation of noise interference, partial information occlusion and missing. A method of shape similarity measurement based on ...
来源: 评论
SAR TARGET CLASSIFICATION WITH CYCLEGAN TRANSFERRED SIMULATED SAMPLES
SAR TARGET CLASSIFICATION WITH CYCLEGAN TRANSFERRED SIMULATE...
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IEEE International geoscience and Remote Sensing Symposium
作者: Lei Liu Zongxu Pan Xiaolan Qiu Lingxiao Peng Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China
Target classification is an important part in automatic target recognition (ATR) systems. Deep learning methods get state of the art performance in SAR target classification. Simulation is a useful data augmentation m... 详细信息
来源: 评论
Curved-path SAR geolocation error analysis based on BP algorithm  38
Curved-path SAR geolocation error analysis based on BP algor...
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38th Annual IEEE International geoscience and Remote Sensing Symposium, IGARSS 2018
作者: Liu, Junbin Qiu, Xiaolan Huang, Lijia Ding, Chibiao Liu, Ming University of Chinese Academy of Sciences China Key Laboratory of Technology in Geo-spatial Information Processing and Application System CAS China Institute of Electronics Chinese Academy of Sciences China National Disaster Reduction Center Ministry of Civil Affairs NDRCC China
The theoretical modeling and analysis of SAR location error play an important role in SAR system design and error source budget. Existing SAR geolocation error models are mainly implicit, which are not easy to do anal... 详细信息
来源: 评论
Spatiotemporal dynamics of coastal dead zones in the Gulf of Mexico over 20 years using remote sensing
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Science of the Total Environment 2025年 979卷
作者: Li, Yingjie Xia, Zilong Nguyen, Lan Wan, Ho Yi Wan, Luwen Wang, Mengqiu Jia, Nan Matli, Venkata Rohith Reddy Li, Yi Seeley, Megan Moran, Emilio F. Liu, Jianguo Center for Systems Integration and Sustainability Department of Fisheries and Wildlife Michigan State University East LansingMI48823 United States Environmental Science and Policy Program Michigan State University East LansingMI48823 United States Natural Capital Project Woods Institute for the Environment Doerr School of Sustainability Stanford University StanfordCA94305 United States Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu Nanjing210023 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Jiangsu Nanjing210023 China Department of Biological Sciences University of Calgary CalgaryABT2N 1N4 Canada Department of Wildlife California State Polytechnic University Humboldt ArcataCA95521 United States Department of Wildlife Ecology and Conservation University of Florida GainesvilleFL32611 United States Department of Earth System Science Stanford University StanfordCA94305 United States Earth and Environmental Sciences Michigan State University East LansingMI48824 United States School of Remote Sensing and Information Engineering Wuhan University Wuhan430072 China Department of Earth Sciences The University of Hong Kong Hong Kong 999077 China Center for Geospatial Analytics North Carolina State University RaleighNC27607 United States College of the Environment and Ecology Xiamen University Xiamen361102 China School of Geographical Sciences and Urban Planning Arizona State University TempeAZ85281 United States Center for Global Discovery and Conservation Science Arizona State University TempeAZ85281 United States Center for Global Change and Earth Observations Michigan State University East LansingMI48824 United States Department of Geography Environment and Spatial Science
Spreading marine dead zones (or hypoxia) are threatening coastal ecosystems and affecting billions of people's livelihoods globally. However, the lack of field observations makes it challenging to estimate dead zo... 详细信息
来源: 评论
Convolutional Neural Network-Based Residue Super-Resolution for Video Coding
Convolutional Neural Network-Based Residue Super-Resolution ...
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IEEE Visual Communications and Image processing (VCIP)
作者: Kang Liu Dong Liu Houqiang Li Feng Wu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China
Inspired by the progress of image and video super-resolution (SR) achieved by convolutional neural network (CNN), we propose a CNN-based residue SR method for video coding. Different from the previous works that opera... 详细信息
来源: 评论
SDM: Semantic Distortion Measurement for Video Encryption
SDM: Semantic Distortion Measurement for Video Encryption
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International Conference on Automatic Face and Gesture Recognition
作者: Yongquan Hu Wei Zhou Shuxin Zhao Zhibo Chen Weiping Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China
Semantic information is important in video encryption. However, existing image quality assessment (IQA) methods, such as the peak signal to noise ratio (PSNR), are still widely applied to measure the encryption securi... 详细信息
来源: 评论
Multiscale Progressive Image Compression Network Guided by Learnable Just Noticeable Distortion
Multiscale Progressive Image Compression Network Guided by L...
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IEEE Visual Communications and Image processing (VCIP)
作者: Xin Jin Runchun Ye Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China
One key challenge to the learning-based image compression is that adaptive bit allocation is crucial for compression effectiveness but can hardly be trained into a neural network. Hereby, in this work, We presents an ... 详细信息
来源: 评论
LEARNED SCALABLE IMAGE COMPRESSION WITH BIDIRECTIONAL CONTEXT DISENTANGLEMENT NETWORK
arXiv
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arXiv 2018年
作者: Zhang, Zhizheng Chen, Zhibo Lin, Jianxin Li, Weiping CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China
In this paper, we propose a learned scalable/progressive image compression scheme based on deep neural networks (DNN), named Bidirectional Context Disentanglement Network (BCD-Net). For learning hierarchical represent... 详细信息
来源: 评论