上海航天（中英文）2025，Vol.42Issue(2)：186-193,8.DOI:10.19328/j.cnki.2096-8655.2025.02.018
基于CNN-LSTM的序列图像空间目标识别方法
CNN-LSTM Based Space Object Recognition Method for Sequence Images
摘要
Abstract
To address the challenge of feature-level fusion in sequence image-based space target recognition,this paper proposes a method combining convolutional neural networks(CNNs)and recurrent neural networks(RNNs)with model improvements.In view of the problem of how to use a single image as a sequence feature input,the CNN is modified,in which the feature maps are used as the sequential inputs.In view of the problem of how the sequence features map to the target categories,the long short-term memory(LSTM)network is modified,in which the output layer is enhanced with a new fully connected layer to predict the target categories.Training with the Gaussian noise levels of 0.001～0.006 and testing at 0.007～0.010 achieve a mean average precision(mAP)improvement from 90.7%to 99.16%.Under different postural conditions,the mAP reaches 94.71%.The model has only 283.0 M parameters,effectively addressing the limitations of result-level fusion in existing methods.关键词
目标识别/序列图像/空间目标/卷积网络(CNN)/循环神经网络(RNN)Key words
object recognition/sequence image/space target/convolutional neural network(CNN)/recurrent neural network(RNN)分类
计算机与自动化引用本文复制引用
齐思宇,赵慧洁,姜宏志,李旭东,王思航,郭琦..基于CNN-LSTM的序列图像空间目标识别方法[J].上海航天（中英文）,2025,42(2):186-193,8.基金项目
航天科技集团应用创新资助项目(6230109004) （6230109004）
