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CORC

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Accurate Building Extraction from Fused DSM and UAV Images Using a Chain Fully Convolutional Neural Network 期刊论文
REMOTE SENSING, 2019, 卷号: 11, 期号: 24, 页码: 18
作者:  Liu, Wei;  Yang, MengYuan;  Xie, Meng;  Guo, Zihui;  Li, ErZhu
收藏  |  浏览/下载：4/0  |  提交时间：2020/05/19
Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs 期刊论文
Remote Sensing, 2019, 卷号: 11, 期号: 20, 页码: 22
作者:  W.X.Zhu;  Z.G.Sun;  Y.H.Huang;  J.B.Lai;  J.Li
收藏  |  浏览/下载：2/0  |  提交时间：2020/08/24
Superresolution for UAV Images via Adaptive Multiple Sparse Representation and Its Application to 3-D Reconstruction 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2017, 卷号: 55, 页码: 4047-4058
作者:  Haris, Muhammad;  Watanabe, Takuya;  Fan, Liu;  Widyanto, Muhammad Rahmat;  Nobuhara, Hajime
收藏  |  浏览/下载：9/0  |  提交时间：2019/12/30
Hierarchical and Adaptive Phase Correlation for Precise Disparity Estimation of UAV Images 期刊论文
IEEE Transactions on Geoscience and Remote Sensing, 2016, 卷号: Vol.54 No.12, 页码: 7092-7104
作者:  Li, J.;  Liu, Y.;  Du, S.;  Wu, P.;  Xu, Z.
收藏  |  浏览/下载：3/0  |  提交时间：2019/02/25
Use of UAV oblique imaging for the detection of individual trees in residential environments 期刊论文
URBAN FORESTRY & URBAN GREENING, 2015
Lin, Yi; Jiang, Miao; Yao, Yunjun; Zhang, Lifu; Lin, Jiayuan
收藏  |  浏览/下载：2/0  |  提交时间：2017/12/03
A ROBUST MATCHING METHOD FOR UNMMANED AERIAL VEHICLE IMAGES WITH DIFFERENT VIEWPOINT ANGLES BASED ON REGIONAL COHERENCY 会议论文
作者:  Yang, Nan;  Li, Congmin;  Shao, Zhenfeng
收藏  |  浏览/下载：3/0  |  提交时间：2019/12/05
Automatic bridge extraction for optical images (EI CONFERENCE) 会议论文
6th International Conference on Image and Graphics, ICIG 2011, August 12, 2011 - August 15, 2011, Hefei, Anhui, China
Gu D.-Y.; Zhu C.-F.; Shen H.; Hu J.-Z.; Chang H.-X.
收藏  |  浏览/下载：16/0  |  提交时间：2013/03/25
This paper describes a novel hierarchy algorithm for extracting bridges over water in optical images. To reduce the omission of bridges by searching the edge  we extract the river regions which the bridges are included in. Firstly  we segment the optical image to get the coarse water bodies using iterative threshold  eliminate the noise regions and add the missing regions based on k-means clustering with texture information and spatial coherence. Then  the blanks are connected based on shape features and candidate bridge regions are segmented from river regions. Finally  the bridges are verified by geometric information and the ubiety between bridges and river. The results show that this approach is efficient and effective for extracting bridges in satellite image from Google Earth and in aerial optical images acquired by unmanned aerial vehicle. 2011 IEEE.  

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