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Train in Dense and Test in Sparse: A Method for Sparse Object Detection in Aerial Images 期刊论文
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 卷号: 19, 页码: 5
作者:  Ding, Kun;  He, Guojin;  Gu, Huxiang;  Zhong, Zisha;  Xiang, Shiming;  Pan, Chunhong
收藏  |  浏览/下载：251/0  |  提交时间：2022/02/16
Convolution  Head  Training  Testing  Object detection  Feature extraction  Real-time systems  Aerial images  object detection  sparse convolution  spatial sparsity  
Gated Feature Aggregation for Height Estimation From Single Aerial Images 期刊论文
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 卷号: 19, 页码: 5
作者:  Xing, Siyuan;  Dong, Qiulei;  Hu, Zhanyi
收藏  |  浏览/下载：217/0  |  提交时间：2022/01/27
Estimation  Decoding  Logic gates  Training  Feature extraction  Testing  Encoding  Convolutional neural networks (CNNs)  gate mechanism  height estimation  progressive refinement  
MCFINet: Multidepth Convolution Network With Shallow-Deep Feature Integration for Semantic Labeling in Remote Sensing Images 期刊论文
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2021, 页码: 5
作者:  Wang, Dongji;  Dong, Qiulei
收藏  |  浏览/下载：129/0  |  提交时间：2022/01/27
Labeling  Convolution  Semantics  Feature extraction  Remote sensing  Kernel  Fuses  Convolutional neural networks  multiscale contexts  remote sensing images  semantic labeling