A Component-Based Multi-Layer Parallel Network for Airplane Detection in SAR Imagery

Remote Sensing

卷:10

Mingxia Tu¹  Dehui Xiong¹  Feng Tu¹  Chu He¹  Mingsheng Liao² 

[1] Electronic and Information School, Wuhan University, Wuhan 430072, China;

[2] State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;

关键词: Synthetic Aperture Radar (SAR);    airplane detection;    deep learning;    component structure;   

DOI  :  10.3390/rs10071016

来源: DOAJ

【 摘 要 】

In this paper, a component-based multi-layer parallel network is proposed for airplane detection in Synthetic Aperture Radar (SAR) imagery. In response to the problems called sparsity and diversity brought by SAR scattering mechanism, depth characteristics and component structure are utilized in the presented algorithm. Compared with traditional features, the depth characteristics have better description ability to deal with diversity. Component information is contributing in detecting complete targets. The proposed algorithm consists of two parallel networks and a constraint layer. First, the component information is introduced into the network by labeling. Then, the overall target and corresponding components are detected by the trained model. In the following discriminative constraint layer, the maximum probability and prior information are adopted to filter out wrong detection. Experiments for several comparative methods are conducted on TerraSAR-X SAR imagery; the results indicate that the proposed network has a higher accuracy for airplane detection.

【 授权许可】

Unknown