CAJ | 학술논문

提出了一种利用深度神经网络的合成孔径雷达图像部分遮挡目标的特征提取和目标识别新方法。该方法首先对合成孔径雷达图像进行预处理，然后提取预处理后合成孔径雷达目标的小波域低频子带图像作为训练数据，最后利用深层稀疏编码模型进一步提取合成孔径雷达遮挡目标的有效特征向量作为目标的特征以完成目标识别。采用 MSTAR数据库中的3类目标进行目标遮挡模拟及识别实验。结果表明，新方法可以综合利用遮挡目标的局部和整体结构信息以提高目标的正确识别率，是一种有效的合成孔径雷达遮挡目标特征提取和目标识别方法。
제출료일충이용심도신경망락적합성공경뢰체도상부분차당목표적특정제취화목표식별신방법。해방법수선대합성공경뢰체도상진행예처리，연후제취예처리후합성공경뢰체목표적소파역저빈자대도상작위훈련수거，최후이용심층희소편마모형진일보제취합성공경뢰체차당목표적유효특정향량작위목표적특정이완성목표식별。채용 MSTAR수거고중적3류목표진행목표차당모의급식별실험。결과표명，신방법가이종합이용차당목표적국부화정체결구신식이제고목표적정학식별솔，시일충유효적합성공경뢰체차당목표특정제취화목표식별방법。
For synthetic aperture radar(SAR) partial occluded images feature extraction and target recognition,a new method based on deep neural networks(DNN) is proposed.After preprocessing original images,we extract low-frequency sub-band images of SAR images in the wavelet domain as training data, and finally make a further extraction of the occluded targets’feature with the deep sparse autoencoder model as the input vectors.Three types of target in MSTAR database are used to simulate target occlusion and recognition experiment.Experimental results prove that the correct recognition rate could be improved by taking advantage of both local and global information about occluded targets,and that the new method is effective for feature extraction and obj ect recognition of occluded SAR targets.