Exo-atmospheric infrared objects classification using recurrence-plots-based convolutional neural networks.

Object discrimination plays an important role in infrared (IR) imaging systems. However, at long observing distance, the presence of detector noise and absence of robust features make exo-atmospheric object classification difficult to tackle. In this paper, a recurrence-plots-based convolutional neural network (RP-CNN) is proposed for feature learning and classification.

Ballistic targets micro-motion and geometrical shape parameters estimation from sparse decomposition representation of infrared signatures.

Micro-motion dynamics and geometrical shape are considered to be essential evidence for infrared (IR) ballistic target recognition. However, it is usually hard or even impossible to describe the geometrical shape of an unknown target with a finite number of parameters, which results in a very difficult task to estimate target micro-motion parameters from the IR signals. Considering the shapes of ballistic targets are relatively simple, this paper explores a joint optimization technique to estimate micro-motion and dominant geometrical shape parameters from sparse decomposition representation of IR irradiance intensity signatures.