Mr. Wei Ma Profile

Wei Ma

at Yunnan Normal Univ

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Publications (2)

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Proceedings Article | 8 July 2022 Paper

SAR image despeckling algorithm using enhanced edge detection in bandelet domain

Zhihui Xin, Mengting Yuan, Zhixu Wang, Yu Sun, Jiayu Xuan, Wei Ma, Yongxin Li

Proceedings Volume 12281, 122810I (2022) https://doi.org/10.1117/12.2620741

KEYWORDS: Synthetic aperture radar, Detection and tracking algorithms, Sensors, Edge detection, Image enhancement, Image filtering, Speckle, Image processing, Wavelet transforms, Digital imaging

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With all-weather, all-time imaging characteristics of synthetic aperture radar (SAR), SAR image is applied widely. However, because of the SAR imaging mechanism, speckle noise is inevitably present in SAR images. In the translationinvariant second-generation bandelet transform (TIBT) domain, SAR image despeckling algorithm using edge detection and feature clustering (CFCM-TIBT) combines edge detection and fuzzy C-main clustering (FCM) operation is an efficient way to reduce speckle noise. However, the edges will be blurred by the algorithm. In order to improve the edge preservation ability and reduce false edge phenomenon. A new algorithm, named enhanced edge detection for SAR images despeckling in TIBT domain (CRFCM-TIBT), is proposed in this letter. It combines CFCM-TIBT and an improved edge detector, named C-RBED, which consists of Canny edge detector and rate-based edge detector (RBED). CRFCM-TIBT better realizes the extraction and separation of details that benefits from the ability of eliminating false edge pixels of C-RBED. The process of CRFCM-TIBT: C-RBED is first utilized to calculate and compare edge direction map (EDM) and edge strong map (ESM) several times. Then, TIBT and FCM are used to decompose and despeckle the edge-removed image, respectively. Finally, add the removed edges to the reconstructed image. In the two scenarios of Bedfordshire and Horse track, the Equivalent Look Number (ENL) and Edge Preservation Index (ESI) of this algorithm are better than traditional Lee filtering, FCM-TIBT and CFCM-TIBT. The experimental results show from the evaluation indicators and visual effects that the method proposed in this paper significantly improves ESI while ensuring a better ENL.

Proceedings Article | 8 July 2022 Paper

SAR image target recognition based on non-local operation

Zhihui Xin, Mengting Yuan, Yongxin Li, Yu Sun, Jiayu Xuan, Wei Ma, Yangxiu Liu, Zhixu Wang

Proceedings Volume 12281, 122810H (2022) https://doi.org/10.1117/12.2620620

KEYWORDS: Detection and tracking algorithms, Synthetic aperture radar, Target recognition, Convolution, Data modeling, Image processing, Feature extraction, Network architectures, Optimization (mathematics), Data processing

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Synthetic aperture radar (SAR) target recognition is an important part of SAR image interpretation. It has been widely used in the field of national defense and national economy. At present, the feature extraction based on convolution is a local operation in space and time. The convolution kernel extracts the features in the local region with a certain step size. The global information of the picture can only be obtained by increasing the number of convolution layers. However, this method will not only increase the difficulty of model training but also makes the optimization of the network more difficult, and even leads to over fitting. Therefore, this paper proposes a SAR image target recognition algorithm based on GoogLeNet -NB, which combines accurate and effective GoogLeNet framework and non-local block(NB). By adding NB to GoogLeNet framework, we can capture more context information, enhance the correlation between pixels and regions, and improve the representation ability of the network. In order to verify the effectiveness of NB, NB is added in different positions of GoogLeNet framework for experimental comparison. Finally, MSTAR database is used to verify the algorithm. The experimental results show that the recognition effect of the GoogLeNet -NB algorithm model proposed in this paper is better than the traditional Alexnet algorithm and GoogLeNet algorithm. In the adding NB in different positions of GoogLeNet framework, adding NB in the front position of GoogLeNet can reduce the loss of information in the training process and obtain more global information. Therefore, GoogLeNet-preNB algorithm has certain advantages over GoogLeNet-postNB algorithm.

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