| Title |
A Study of CR-DuNN based on the LSTM and Du-CNN to Predict Infrared Target Feature and Classify Targets from the Clutters |
| DOI |
http://doi.org/10.5370/KIEE.2019.68.1.153 |
| Keywords |
Infrared image ; Coast targets ; Long short term memory ; Convolutional neural network |
| Abstract |
In this paper, we analyze the infrared feature for the small coast targets according to the surrounding environment for autonomous flight device equipped with an infrared imaging sensor and we propose Cross Duality of Neural Network (CR-DuNN) method which can classify the target and clutter in coastal environment. In coastal environment, there are various property according to diverse change of air temperature, sea temperature, deferent seasons. And small coast target have various infrared feature according to diverse change of environment. In this various environment, it is very important thing that we analyze and classify targets from the clutters to improve target detection accuracy. Thus, we propose infrared feature learning algorithm through LSTM neural network and also propose CR-DuNN algorithm that integrate LSTM prediction network with Du-CNN classification network to classify targets from the clutters. |