Directional support value of Gaussian transformation for infrared small target detection

Robust small target detection is one of the key techniques in IR search and tracking systems for self-defense or attacks. In this paper we present a robust solution for small target detection in a single IR image. The key ideas of the proposed method are to use the directional support value of Gaussian transform (DSVoGT) to enhance the targets, and use the multiscale representation provided by DSVoGT to reduce the false alarm rate. The original image is decomposed into sub-bands in different orientations by convolving the image with the directional support value filters, which are deduced from the weighted mapped least-squares-support vector machines (LS-SVMs). Based on the sub-band images, a support value of Gaussian matrix is constructed, and the trace of this matrix is then defined as the target measure. The corresponding multiscale correlations of the target measures are computed for enhancing target signal while suppressing the background clutter. We demonstrate the advantages of the proposed method on real IR images and compare the results against those obtained from standard detection approaches, including the top-hat filter, max-mean filter, max-median filter, min-local-Laplacian of Gaussian (LoG) filter, as well as LS-SVM. The experimental results on various cluttered background images show that the proposed method outperforms other detectors.