Multi-Scale Convolutional Neural Networks for Space Infrared Point Objects Discrimination
Object discrimination plays an important role in an infrared (IR) imaging system. However, at a long observing distance, the presence of detector noise and the absence of robust features make space objects' discrimination difficult to tackle with. In this paper, a multi-scale convolutional neur...
Main Authors: | Qiuqun Deng, Huanzhang Lu, Huamin Tao, Moufa Hu, Fei Zhao |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8636952/ |
Similar Items
-
Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification
by: Dongya Wu, et al.
Published: (2019-10-01) -
Object Tracking Based on Vector Convolutional Network and Discriminant Correlation Filters
by: Yuan Liu, et al.
Published: (2019-04-01) -
Estimating Shape and Micro-Motion Parameter of Rotationally Symmetric Space Objects from the Infrared Signature
by: Yabei Wu, et al.
Published: (2016-10-01) -
Multiple-Model Fully Convolutional Neural Networks for Single Object Tracking on Thermal Infrared Video
by: Mohd Asyraf Zulkifley, et al.
Published: (2018-01-01) -
Multiple space based cascaded center point network for object detection
by: Zhiqiang Jiang, et al.
Published: (2023-06-01)