- 期刊
- OpenAccess
Automatic Target Classification in SAR Images by Multilayer Back Propagation Neural Network
並列摘要
In this study, a novel descriptive feature extraction method of Discrete Fourier transform and neural network classifier for classification of Synthetic Aperture Radar (SAR) images is proposed. The classification process has the following stages (1) Image Segmentation using statistical Region Merging (SRM) (2) Polar transform and Feature extraction using Discrete Fourier Transform (3) Neural Network classification using back propagation. The algorithm has been applied for the three classes of military manmade objects (metal objects) in SAR imagery is using MSTAR public release database. Experimental results are presente.
延伸閱讀
- 陳品妤（2020）。Effects of Image Augmentation on Deep Learning Methods〔碩士論文，中原大學〕。華藝線上圖書館。https://doi.org/10.6840/cycu202000379
- 劉興漢、楊顓豪、周世淵（2023）。A Steganalysis Method Based on the Back Propagation Neural Network for Digital Images in the Spatial Domain。國防管理學報，44(1)，77-97。https://www.airitilibrary.com/Article/Detail?DocID=10224858-N202305260010-00004
- 王仁駿（2019）。Image-based Traffic Characteristics Extraction through Deep Learning〔碩士論文，國立臺灣大學〕。華藝線上圖書館。https://doi.org/10.6342/NTU201900418
- Huang, S., Huang, W., & Zhang, T. (2016). A Change Detection Method of Multi-temporal SAR Images Based on Contourlet Transform. International Journal of Remote Sensing Applications, 6(), 53-64. https://doi.org/10.14355/ijrsa.2016.06.006
- 張仁明、林達德（1999）。Comparative Studies on Image Segmentation Using Neural Network and Bayesian Classification Method。農業機械學刊，8(3)，61-74。https://doi.org/10.30062/JAM.199909.0006