Validation of PCA and LDA for SAR ATR

Abstract

Both principal component analysis (PCA) and linear discriminant analysis (LDA) have long been recognized as tools for feature extraction and data analysis. There has been reports in the open literature regarding the performance of both LDA and PCA as feature extractors in various types of classification and recognition problems. Many of the reports claim a better performance with LDA than with PCA. However, the grounds of comparison have mostly been quite narrow. In the current paper PCA and LDA based classifiers are evaluated for the problem of synthetic aperture radar based automatic target recognition problem. The results show that in terms of absolute performance, PCA outperforms LDA. Results of PCA based classifier are also found to be of higher confidence than those from LDA based classifiers, as observed from the error-bar analysis of the classifiers. With decreased amount of training dataset, the degradation in the performance of the classifiers are almost similar in nature. The current work concludes that LDA is not suitable for radar image based target recognition task. This is in line with reports from some works in the open literature which claim that the success of LDA will depend on the type of data and whether there is exhaustive data available during the training phase or not.

SAR Target Recognition via Supervised Discriminative Dictionary Learning and Sparse Representation of the SAR-HOG Feature

“…Many classic classifiers have been used in SAR ATR, such as SVM [1], kNN [3], etc. The earlier mentioned dimensionality reduction methods PCA and LDA can also be taken as classifiers [4]. Recently, the sparse representation classifier proposed by Wright [10] has been successfully applied to SAR applications, such as polarimetric SAR image classification [20] and SAR ATR [3].…”

“…Although many works have been done in the past few decades [1][2][3][4][5][6][7][8], it is still a highly challenging problem. Generally, the process of SAR ATR includes four sequential stages: detection, discrimination, feature extraction and classification.…”

Kernel generalized neighbor discriminant embedding for SAR automatic target recognition

“…1) Compute the affinity weight matrix W according to (2), compute kernel matrix K according to (9). 2) According to (11) and (12), compute the object matrix M K , resolve the maximization problem as (15) and get the optimal embedding map A.…”

“…Principal component analysis (PCA) and linear discriminant analysis (LDA) were used for SAR image feature extraction [1,2] because of their simplicity and effectiveness. Both of them are based on a global linear structure and need to transform a two-dimensional image into a one-dimensional vector.…”

A Modified AdaBoost Algorithm with New Discrimination Features for High-Resolution SAR Targets Recognition

“…Principal component analysis (PCA) and linear discrimination analysis (LDA) [2] nel method and the manifold learning method, such as support vector machine (SVM) [3] and locally linear embedding (LLE) [4]. Concerning Boost, the literature reported many success of Boost algorithm for pattern recognition, including Ada Boost, Logit Boost, Grad Boost and Taylor Boost [5].…”