Exo-atmospheric infrared objects classification usingrecurrence-plots-based convolutional neural networks
2019
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.
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