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Lithology classification is a crucial step in the prospecting process, and polarimetric synthetic aperture radar (Pol-SAR) imagery has been extensively used for it. However, despite significant improvements in both information content of Pol-SAR imagery and advanced classification approaches, lithology classification using Pol-SAR data may not provide satisfactory classification accuracy due to high similarity of certain classes. In this paper, a novel Pol-SAR lithology classification method based on a stacked sparse autoencoder (SSAE) is proposed. By using superpixel segmentation, new features can be extracted from dual-frequency Pol-SAR data, which can increase the class separability of the input data. Then, these features and the coherency matrices are incorporated into SSAE to classify the lithology. The classification performance is evaluated on an SIR-C dataset acquired over Xinjiang, China. The experimental result shows that this method is effective for lithology classification and can improve the overall accuracy up to 98.90%.
Wenguang Wang; Xin Ren; Yan Zhang; Meng Li. Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data. Applied Sciences 2018, 8, 1513 .
AMA StyleWenguang Wang, Xin Ren, Yan Zhang, Meng Li. Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data. Applied Sciences. 2018; 8 (9):1513.
Chicago/Turabian StyleWenguang Wang; Xin Ren; Yan Zhang; Meng Li. 2018. "Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data." Applied Sciences 8, no. 9: 1513.
Multi-Unmanned Aerial Vehicle (UAV) Doppler-based target tracking has not been widely investigated, specifically when using modern nonlinear information filters. A high-degree Gauss–Hermite information filter, as well as a seventh-degree cubature information filter (CIF), is developed to improve the fifth-degree and third-degree CIFs proposed in the most recent related literature. These algorithms are applied to maneuvering target tracking based on Radar Doppler range/range rate signals. To achieve this purpose, different measurement models such as range-only, range rate, and bearing-only tracking are used in the simulations. In this paper, the mobile sensor target tracking problem is addressed and solved by a higher-degree class of quadrature information filters (HQIFs). A centralized fusion architecture based on distributed information filtering is proposed, and yielded excellent results. Three high dynamic UAVs are simulated with synchronized Doppler measurement broadcasted in parallel channels to the control center for global information fusion. Interesting results are obtained, with the superiority of certain classes of higher-degree quadrature information filters.
Hamza Benzerrouk; Alexander Nebylov; Meng Li. Multi-UAV Doppler Information Fusion for Target Tracking Based on Distributed High Degrees Information Filters. Aerospace 2018, 5, 28 .
AMA StyleHamza Benzerrouk, Alexander Nebylov, Meng Li. Multi-UAV Doppler Information Fusion for Target Tracking Based on Distributed High Degrees Information Filters. Aerospace. 2018; 5 (1):28.
Chicago/Turabian StyleHamza Benzerrouk; Alexander Nebylov; Meng Li. 2018. "Multi-UAV Doppler Information Fusion for Target Tracking Based on Distributed High Degrees Information Filters." Aerospace 5, no. 1: 28.