Dim Moving Point Target Detection in Cloud Clutter Scenes Based on Temporal Profile Learning

This letter presents an approach for detecting dim moving point targets in cloud clutter scenes based on temporal profile (TP) learning. The main idea is that a weak transient disturbance will appear in the TPs of target-present pixels, changing the TP's characteristics. We propose a novel signal-to-signal network to learn the temporal characteristics of the background and the clutter, in which the transient disturbance is extracted by the residual between the input signal and the reconstructed background and the clutter signal. The structure of the ConvBlock-1-D is designed to enhance the flow and propagation of features in layers. A loss function is proposed to solve the imbalance problem. We provided a comparison to other widely used methods by using simulated datasets and real-world image sequences. The experimental results demonstrate that our method has the best performance in terms of qualitative and quantitative assessments.