Prof. Qiang Yang Profile

Prof. Qiang Yang

at Harbin Institute of Technology

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Proceedings Article | 12 October 2022 Paper

SAR target image edge detection based on CNN

Wozhan Li, Xiaochuan Wu, Qiang Yang

Proceedings Volume 12342, 123420C (2022) https://doi.org/10.1117/12.2643482

KEYWORDS: Edge detection, Synthetic aperture radar, Target detection, Network architectures, Detection and tracking algorithms, Radar, Image processing, Scattering, Neural networks, Image fusion

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Aiming at the problems that the classical edge detection method is easily affected by noise and has low detection accuracy when applied to SAR target images, this paper studies the detection performance of the classical edge detection method Canny, CNN-based edge detection methods Holistically Nested Edge Detection (HED) and Richer Convolutional Features (RCF) when applied to SAR target images for the first time. The detection performance is evaluated using the MSTAR dataset, and the detection results of each method are compared based on the common evaluation indicators of image edge detection: F-measure, PR curve, and FPS. Canny's F-measure (ODS) is 0.611 and FPS is 43. The F-measure (ODS) of HED is 0.758 and the FPS is 18. The F-measure (ODS) of RCF is 0.729 and the FPS is 24. The F-measure (ODS) of RCF-MS is 0.753 and the FPS is 6. On the MSTAR dataset, the F-measure of HED is the best, which is 24.06% higher than Canny. RCF and RCF-MS also performed well, which were 19.31% and 23.24% higher than Canny respectively. The edge detection method based on CNN has higher F-measure, is less affected by noise, and has less loss of edge details. When applied to SAR images affected by speckle noise, the performance is much better than Canny, but there is still a shortage of slightly worse computing speed.

Proceedings Article | 9 August 2018 Paper

A new two stages ATR architecture based on sparse auto encoder and learning network

Mohamed Touafria, Qiang Yang

Proceedings Volume 10806, 108060J (2018) https://doi.org/10.1117/12.2502944

KEYWORDS: Synthetic aperture radar, Convolution, Automatic target recognition, Detection and tracking algorithms, Network architectures

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This article discusses the issue of automatic target recognition (ATR) on Synthetic Aperture Radar images (SAR). Through learning the hierarchy of features automatically from massive training data, learning networks, such as Convolutional Neural Networks (CNN) has recently achieved the state-of-the-art results in many tasks. Moreover, unlike optical images, SAR imaging have the advantages of reduced sensitivity to weather conditions, day-night operation, penetration capability through obstacles, etc. Despite these utilities, several factors can affect the accuracy of the classification, such as errors linked with brightness values of the pixels and geometry registered by the satellite sensors. To correct these errors and extract better features about SAR targets, and obtain better accuracies a two steps algorithm called SAE-CNN-Recognizer(SCR) is proposed: Firstly, a pre-processing step consist of image enhancement is achieved using Sparse Auto-Encoder (SAE) to emphasize some image features for following analysis. Secondly, CNN architecture which consist of a feature extraction stage followed by a classification step using a softmax classifier. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove that this approach can accomplish an average accuracy higher than 97% on the classification of targets in ten categories, which is higher than the traditional CNN results.

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