Infrared Small Target Detection Based on Singularity Analysis and Constrained Random Walker

Effective infrared small target detection is still challenging due to small target sizes and the clutter in the background. Unfortunately, many advanced methods do not perform well in preserving and detecting multi-scale objects in complex scenes. We propose an infrared small targets method to suppress the background and adapt the infrared small targets with different sizes. Based on the singular value analysis in the facet model, we propose a multi-derivative descriptor to enhance the targets and suppress various clutter in the dual derivative channels. In the first-order derivative channel, we design four facet kernels with different directions to enhance and preserve the isotropic small targets and suppress the block clutter. In the second-order derivative channel, we use the facet kernel to enhance the center pixels of targets and suppress the band clutter. In order to adapt to the targets with various sizes, we propose a constrained random walker technique, including an adaptive matching algorithm to extract the local regions of each candidate adaptively based on the constraint of size and shape. The experimental results demonstrate that the proposed method can accurately detect multi-scale small targets in complex scenes, resulting in better detection performance than the state-of-the-art methods.