Kernel generalized neighbor discriminant embedding for SAR automatic target recognition
نویسندگان
چکیده
منابع مشابه
Kernel generalized neighbor discriminant embedding for SAR automatic target recognition
In this paper, we propose a new supervised feature extraction algorithm in synthetic aperture radar automatic target recognition (SAR ATR), called generalized neighbor discriminant embedding (GNDE). Based on manifold learning, GNDE integrates class and neighborhood information to enhance discriminative power of extracted feature. Besides, the kernelized counterpart of this algorithm is also pro...
متن کاملPose Estimation for SAR Automatic Target Recognition
This paper explores statistically pose estimation in SAR ATR. Based on our proposed method of maximizing mutual information, further experiments are conducted by using the new MSTAR/ IU Database. Different pose estimator topologies and training criteria are also employed. Experimental results show that our proposed method reduces the average pose estimation error to within 10 degrees of the tru...
متن کاملDiscriminant Projections Embedding for Nearest Neighbor Classification
In this paper we introduce a new embedding technique to linearly project labeled data samples into a new space where the performance of a Nearest Neighbor classifier is improved. The approach is based on considering a large set of simple discriminant projections and finding the subset with higher classification performance. In order to implement the feature selection process we propose the use ...
متن کاملKernel Uncorrelated Discriminant Analysis for Radar Target Recognition
Kernel fisher discriminant analysis (KFDA) has received extensive study in recent years as a dimensionality reduction technique. KFDA always encounters an intrinsic singularity of scatter matrices in the feature space, namely ‘small sample size’ (SSS) problem. Several novel methods have been proposed to cope with this problem. In this paper, kernel uncorrelated discriminant analysis (KUDA) is p...
متن کاملJoint Embedding and Classification for SAR Target Recognition
Deep learning can be an effective and efficient means to automatically detect and classify targets in synthetic aperture radar (SAR) images, but it is critical for trained neural networks to be robust to variations that exist between training and test environments. The layers in a neural network can be understood to be successive transformations of an input image into embedded feature represent...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: EURASIP Journal on Advances in Signal Processing
سال: 2014
ISSN: 1687-6180
DOI: 10.1186/1687-6180-2014-72