… use the attributed scattering centers (ASCs) extracted from the target echoes as the components
of the target for SAR ATR, which divides the SAR … on the complex SAR data, PCA [6], the …

… the SAR image in the complex wavelet domain, we proposed to use the LPG-PCA method
… For comparison, we used several state-of-the-art despeckling methods. The first one is the …

… The PCA method attention can be drawn to the high variant speckle present in the … In
this paper, we use a set of GMMs that evolves with the background along the images in each …

… more specific, unlike the existing SAR ATR that uses limited data only based on label …
PCA+SVM, LCKSVD, DGM, and AdaBoost [35] are traditional algorithms. The two DNNs [36]…

… brightest scatterer from kernel PCA. Simulations demonstrate … summarizing the state of the
art, enumerating the challenges, … in SAR tomography than in conventional BSS applications, …

… The manifold learning methods including principal component analysis (PCA) [9], [10] and …
applies it to SAR image feature extraction in order to enhance SAR ATR performance. We set …

… The conventional classification models of SAR automatic target recognition (ATR) comprise
… A template-based SAR-ATR framework uses least mean square error (MSE) measures to …

… 1 uses SVM as the classifier and PCA for feature extraction. Comparison Method 2 uses …
Comparison Method 3 uses KSRC as the classifier and PCA for feature extraction. It can be …

… is the key component to improve the SAR ATR performance. … noise via the robust principal
component analysis (RPCA) [25]… ; thus, our implementation conforms to the specification of the …

… a particular ATR model and its performance characteristics. Indeed, many models in active
use today are … Each baseline technique was paired with Principal Component Analysis (PCA) …