机构

• 西安光学精密机械研究... [2]
• 计算技术研究所 [1]
• 长春光学精密机械与物... [1]
• 自动化研究所 [1]

采集方式

• OAI收割 [5]

内容类型

• 期刊论文 [3]
• 会议论文 [2]

发表日期

• 2023 [1]
• 2022 [2]
• 2019 [1]
• 2006 [1]

学科主题

筛选

浏览/检索结果: 共5条，第1-5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 Spectral-Spatial Attention Rotation-Invariant Classification Network for Airborne Hyperspectral Images 期刊论文  OAI收割 DRONES, 2023, 卷号: 7, 期号: 4 作者:  Shi, Yuetian;  Fu, Bin;  Wang, Nan;  Cheng, Yinzhu;  Fang, Jie   |  收藏  |  浏览/下载：42/0  |  提交时间：2023/05/29 airborne hyperspectral image  hyperspectral image classification  rotation-invariant  local spatial feature enhancement  convolutional neural network  attention mechanism  lightweight feature enhancement   Gradient-Aligned convolution neural network 期刊论文  OAI收割 PATTERN RECOGNITION, 2022, 卷号: 122, 页码: 10 作者:  Hao, You;  Hu, Ping;  Li, Shirui;  Udupa, Jayaram K.;  Tong, Yubing   |  收藏  |  浏览/下载：39/0  |  提交时间：2021/12/01 Gradient alignment  Rotation equivariant convolution  Rotation invariant neural network   Rotation-Invariant Attention Network for Hyperspectral Image Classification 期刊论文  OAI收割 IEEE Transactions on Image Processing, 2022, 卷号: 31, 页码: 4251-4265 作者:  Zheng, Xiangtao;  Sun, Hao;  Lu, Xiaoqiang;  Xie, Wei   |  收藏  |  浏览/下载：33/0  |  提交时间：2022/07/21 Hyperspectral image classification  convolutional neural network  rotation-invariant network  spectralspatial feature extraction  attention mechanism   Rotaion and Scale-invariant Object Detector for High Resolution Optical Remote Sensing Images 会议论文  OAI收割 日本横滨, 2019年7月29日-2019年8月2日 作者:  Huang H(黄河);  Huo CL(霍春雷);  Wei FL(魏飞龙);  Pan CH(潘春洪)   |  收藏  |  浏览/下载：73/0  |  提交时间：2019/06/24 Rotation-invariant  Scale-invariant  Convolutional Neural Network  Optical Remote Sensing  Object Detection   An object recognition method based on fuzzy theory and BP networks (EI CONFERENCE) 会议论文  OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China Chuan W.; Ming Z.; Dong Y. 收藏  |  浏览/下载：19/0  |  提交时间：2013/03/25 It is difficult to choose eigenvectors when neural network recognizes object. It is possible that the different object eigenvectors is similar or the same object eigenvectors is different under scaling  shifting  rotation if eigenvectors can not be chosen appropriately. In order to solve this problem  the image is edged  the membership function is reconstructed and a new threshold segmentation method based on fuzzy theory is proposed to get the binary image. Moment invariant of binary image is extracted and normalized. Some time moment invariant is too small to calculate effectively so logarithm of moment invariant is taken as input eigenvectors of BP network. The experimental results demonstrate that the proposed approach could recognize the object effectively  correctly and quickly.