Neural Network Classification Method for Aircraft in Isar Images

Abstract

<jats:p>This article offers a neural network method for automatic classification of Inverse Synthetic Aperture Radar objects represented in images with high level of post-receive optimization. A full explanation of the procedures of two-layer neural network architecture creating and training is described. The classification in the recognition stage is proposed, based on several main classes or sets of flying objects. The classification sets are designed according to distinctive specifications in the structural models of the aircrafts. The neural network is experimentally simulated in MATLAB environment. Numerical results of the experiments carried, prove the correct classification of the objects in ISAR optimized images.</jats:p>

Domain Adaptive Few-Shot Learning for ISAR Aircraft Recognition with Transferred Attention and Weighting Importance

“…Aircraft recognition is one of the important tasks in ISAR image identification [1][2][3][4][5][6]. In recent years, deep convolution neural networks have achieved better performance in ISAR image classification [7][8][9][10][11][12][13][14], which can be applied to aircraft recognition tasks.…”