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. First, it uses recurrence plots (RPs) to transform time sequences of IR radiation into two-dimensional texture images. Then, a CNN model is adopted for classification. Different from previous object classification methods, RP representation has well-defined visual texture patterns, and their graphical nature exposes hidden patterns and structural changes in time sequences of IR signatures. In addition, it can process IR signatures of objects without the limitation of fixed length. Training data are generated from IR irradiation models considering micro-motion dynamics and geometrical shape of exo-atmospheric objects. Results based on time-evolving IR radiation data indicate that our method achieves significant improvement in accuracy and robustness of the exo-atmospheric IR objects 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. By dividing an observed target surface into a number of segmented patches, an IR signature of the target can be approximately modeled as a linear combination of the observation IR signatures from the dominant segmented patches. Given this, a sparse decomposition representation of the IR signature is established with the dictionary elements defined as each segmented patch's IR signature. Then, an iterative optimization method, based on the batch second-order gradient descent algorithm, is proposed to jointly estimate target micro-motion and geometrical shape parameters. Experimental results demonstrate that the micro-motion and geometrical shape parameters can be effectively estimated using the proposed method, when the noise of the IR signature is in an acceptable level, for example, SNR>0 dB.