中文版 | English

在结果中检索

OPT OpenIR
（本次检索基于用户作品认领结果）

浏览/检索结果: 共5条，第1-5条 帮助

限定条件            
已选(0)清除 条数/页：   排序方式：
Cross-Attention Spectral-Spatial Network for Hyperspectral Image Classification 期刊论文
IEEE Transactions on Geoscience and Remote Sensing, 2022, 卷号: 60
作者:  Yang, Kai;  Sun, Hao;  Zou, Chunbo;  Lu, Xiaoqiang
Adobe PDF(3919Kb)  |  收藏  |  浏览/下载：174/2  |  提交时间：2022/01/25
Hyperspectral image classification  convolutional neural networks  spectral attention  spatial attention  
Spectral-Spatial Attention Network for Hyperspectral Image Classification 期刊论文
IEEE Transactions on Geoscience and Remote Sensing, 2020, 卷号: 58, 期号: 5, 页码: 3232-3245
作者:  Sun, Hao;  Zheng, Xiangtao;  Lu, Xiaoqiang;  Wu, Siyuan
Adobe PDF(3278Kb)  |  收藏  |  浏览/下载：346/3  |  提交时间：2020/05/20
Attention  convolutional neural network (CNN)  hyperspectral image (HSI) classification  spectral–spatial feature extraction  
Exploiting Embedding Manifold of Autoencoders for Hyperspectral Anomaly Detection 期刊论文
IEEE Transactions on Geoscience and Remote Sensing, 2020, 卷号: 58, 期号: 3, 页码: 1527-1537
作者:  Lu, Xiaoqiang;  Zhang, Wuxia;  Huang, Ju
Adobe PDF(1670Kb)  |  收藏  |  浏览/下载：174/2  |  提交时间：2020/03/23
Autoencoder (AE)  global reconstruction  hyperspectral imagery (HSI)  hyperspetral anomaly detection  local reconstruction  manifold learning  
A Deep Scene Representation for Aerial Scene Classification 期刊论文
IEEE Transactions on Geoscience and Remote Sensing, 2019, 卷号: 57, 期号: 7, 页码: 4799-4809
作者:  Zheng, Xiangtao;  Yuan, Yuan;  Lu, Xiaoqiang
Adobe PDF(3061Kb)  |  收藏  |  浏览/下载：213/3  |  提交时间：2019/07/17
Aerial scene classification  convolutional neural networks (CNNs)  Fisher vector (FV)  multiscale representation  
Mutual Attention Inception Network for Remote Sensing Visual Question Answering 期刊论文
IEEE Transactions on Geoscience and Remote Sensing
作者:  Zheng, Xiangtao;  Wang, Binqiang;  Du, Xingqian;  Lu, Xiaoqiang
Adobe PDF(6830Kb)  |  收藏  |  浏览/下载：72/0  |  提交时间：2021/06/22
Attention mechanism  feature fusion  remote sensing visual question answering (RSVQA)  semantic understanding