Building Regional Covariance Descriptors for Vehicle Detection

We study the question of building regional covariance descriptors (RCDs) for vehicle detection from high-resolution satellite images. A unified way is proposed to build RCD features by constant convolutional kernels in the forms of 2-D masks. Two novel formulas are designed to construct different RCD types based upon one or two convolutional masks, obtaining ten novel RCD features by four simple constant convolutional masks. Experiments show that such convolutional-mask-based RCDs outperform the previous image-derivative-based RCDs, the popular local binary patterns (LBPs), the histogram of oriented gradients (HOGs), and LBP+HOG. Furthermore, feeding to nonlinear support vector machines (SVMs) of two kernel types [L 1 kernel and radial basis function (RBF)], these RCDs outperform four known deep convolutional neural networks: AlexNet, GoogLeNet, CaffeNet, and LeNet, as well as their fine-tuned models by their well-trained weights of imageNet classification. Among three popular classic classifiers we have tested in the experiments, nonlinear SVMs outperform BP and Adaboost obviously, and L 1 kernel exceeds RBF slightly.