MF-DCMANet: A Multi-Feature Dual-Stage Cross Manifold Attention Network for PolSAR Target Recognition

Abstract

The distinctive polarization information of polarimetric SAR (PolSAR) has been widely applied to terrain classification but is rarely used for PolSAR target recognition. The target recognition strategies built upon multi-feature have gained favor among researchers due to their ability to provide diverse classification information. The paper introduces a robust multi-feature cross-fusion approach, i.e., a multi-feature dual-stage cross manifold attention network, namely, MF-DCMANet, which essentially relies on the complementary information between different features to enhance the representation ability of targets. In the first-stage process, a Cross-Feature-Network (CFN) module is proposed to mine the middle-level semantic information of monogenic features and polarization features extracted from the PolSAR target. In the second-stage process, a Cross-Manifold-Attention (CMA) transformer is proposed, which takes the input features represented on the Grassmann manifold to mine the nonlinear relationship between features so that rich and fine-grained features can be captured to compute attention weight. Furthermore, a local window is used instead of the global window in the attention mechanism to improve the local feature representation capabilities and reduce the computation. The proposed MF-DCMANet achieves competitive performance on the GOTCHA dataset, with a recognition accuracy of 99.75%. Furthermore, it maintains a high accuracy rate in the few-shot recognition and open-set recognition scenarios, outperforming the current state-of-the-art method by about 2%.

Object detection in optical imaging of the Internet of Things based on deep learning

“…As AI advances, a paradigm shift from conventional feature extraction techniques to DL algorithms, particularly CNNs, has emerged. Utilizing CNNs for feature extraction has led to heightened efficiency and accuracy in detecting targets within IoT systems ( Li et al, 2023a ). Notably, Li et al (2023a) and Li et al (2023b) demonstrated that a multi-scale analysis modulation recognition network employing denoising encoders, deep adaptive threshold learning, and multi-scale feature fusion significantly improved recognition accuracy in low signal-to-noise ratio environments.…”

“…Utilizing CNNs for feature extraction has led to heightened efficiency and accuracy in detecting targets within IoT systems ( Li et al, 2023a ). Notably, Li et al (2023a) and Li et al (2023b) demonstrated that a multi-scale analysis modulation recognition network employing denoising encoders, deep adaptive threshold learning, and multi-scale feature fusion significantly improved recognition accuracy in low signal-to-noise ratio environments. Meanwhile, the network shows the adaptive learning ability of different noise thresholds and the advantages of effective feature fusion modules under various modulation types ( Li et al, 2023b ).…”

An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System

“…Based on the synthetic aperture principle and making use of the Doppler information generated by the relative motion of the radar platform and the target being detected, spaceborne synthetic aperture radar (SAR) is a high-resolution microwave imaging technology [1][2][3][4]. It has a variety of characteristics, such as all-time, all-weather, high-resolution large width, and has a certain degree of surface penetration, offering it a unique advantage in applications such as disaster monitoring, marine monitoring, resource surveying, mapping and military [5][6][7][8][9]. Recent publications provide an overview of the application of satellite remote sensing technology to natural hazards such as earthquakes, volcanoes, floods, land-slides, and coastal flooding [10][11][12].…”