Abstract
A synthetic aperture radar (SAR) target recognition method based on image blocking and matching is proposed. The test SAR image is first separated into four blocks, which are analyzed and matched separately. For each block, the monogenic signal is employed to describe its time-frequency distribution and local details with a feature vector. The sparse representation-based classification (SRC) is used to classify the four monogenic feature vectors and produce the reconstruction error vectors. Afterwards, a random weight matrix with a rich set of weight vectors is used to linearly fuse the feature vectors and all the results are analyzed in a statistical way. Finally, a decision value is designed based on the statistical analysis to determine the target label. The proposed method is tested on the moving and stationary target acquisition and recognition (MSTAR) dataset and the results confirm the validity of the proposed method.
Highlights
High-resolution synthetic aperture radar (SAR) images provide basis for efficient and accurate intelligence interpretation [1]. e moving and stationary target acquisition and recognition (MSTAR) dataset provided a benchmark for the research and evaluation of SAR target recognition algorithms [2, 3]. e resolution of SAR images in this dataset reaches 0.3 m, which can be effectively used for the classification of vehicle targets such as tanks, armored vehicles, and cannons
For the extracted monogenic features, sparse representation classification (SRC) is adopted as the classifier [27,28,29]. e idea of sparse representation assumes that the test sample can be linearly reconstructed by the training samples from the same class
E fourth one developed a novel convolutional neural networks (CNN) architecture, namely, all fully convolutional neural network (A-ConvNet), for SAR target recognition [31], which is chosen as a representation for deep learning-based algorithms. e following tests are conveyed under both standard operating condition (SOC) and extended operating conditions (EOCs) to provide comprehensive evaluation of the proposed method
Summary
High-resolution synthetic aperture radar (SAR) images provide basis for efficient and accurate intelligence interpretation [1]. e moving and stationary target acquisition and recognition (MSTAR) dataset provided a benchmark for the research and evaluation of SAR target recognition algorithms [2, 3]. e resolution of SAR images in this dataset reaches 0.3 m, which can be effectively used for the classification of vehicle targets such as tanks, armored vehicles, and cannons. E deep learning models are directly trained and learned based on the original images, avoiding the traditional manual feature extraction process. The corrupted test sample can still share high similarities with the corresponding sample from the actual training class In this sense, by observing and evaluating the local differences and consistency between SAR images, EOCs can be overcome with high effectiveness. Erefore, it is meaningful to fully investigate the local changes of the target as for handling the EOCs. Traditional methods were generally developed based on overall SAR images for feature extraction and classification. Traditional methods were generally developed based on overall SAR images for feature extraction and classification In this case, the local changes may cause variations of global feature changes. It is beneficial to obtain the true correlation between the test sample and the training classes, improving the classification accuracy
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
