This study investigates the application of Tensor Robust Principal Components analysis (TRPCA) as a pre-processing tool in classifying oil rigs using synthetic aperture radar (SAR) images. The pre-processing considers the tensor composition of original images and subsequent attribute extraction using the VGG-16 convolutional neural network from the low-rank and sparse images. The extracted features are then classified using Support Vector Machine (SVM), Neural Network (NET), and Logistic Regression (LR) classifiers. The experimental evaluation utilized C-band VH-polarization SAR images from the Sentinel-1 system. The findings indicate that TRPCA-based pre-processing enhances classification accuracy, outperforming existing methods documented in the literature. © 2024 IEEE.