Abstract
Infrared sensing technology can be well used for night observation, which is becoming an important measure for battlefield reconnaissance. It is a powerful way to implement precision strikes and situational awareness by improving the ability of target recognition based on infrared images. For the problem of infrared image recognition, the Light Gradient Boosting Machine (LightGBM) is employed to select the outline descriptors extracted based on the elliptic Fourier series (EFS), which is combined with sparse representation-based classification (SRC) to achieve target recognition. First, based on the target outlines in the infrared image, the multi-order outline descriptors are extracted to characterize the essential characteristics of the target to be recognized. Then, the LightGBM feature selection algorithm is used to screen the multi-order outline descriptors to reduce redundancy and improve the pertinence of features. Finally, the selected outline descriptors are classified based on SRC. The method effectively improves the effectiveness of the final features through the feature selection of LightGBM and reduces the computational complexity of classification at the same time, which is beneficial to improve the overall recognition performance. The mid-wave infrared (MWIR) dataset of various targets is employed to carry out verification experiments for the proposed method under three different conditions of original samples, noisy samples, and partially occluded samples. By comparing the proposed method with several types of existing infrared target recognition methods, the results show that the proposed method can achieve better performance.
Highlights
Infrared sensing technology can be well used for night observation, which is becoming an important measure for battlefield reconnaissance
The selected outline descriptors are classified based on sparse representation-based classification (SRC). e method effectively improves the effectiveness of the final features through the feature selection of LightGBM and reduces the computational complexity of classification at the same time, which is beneficial to improve the overall recognition performance
Dataset. e experiments are carried out on the MWIR dataset, which contains images of 10 typical military targets acquired by the mid-wave infrared sensors
Summary
Infrared sensing technology can be well used for night observation, which is becoming an important measure for battlefield reconnaissance. This paper employs the Light Gradient Boosting Machine (LightGBM) [16,17,18,19,20] for the selection of outline descriptors extracted by elliptic Fourier series (EFS) [21,22,23] and applies it in infrared image target recognition. In the feature extraction stage, the first step is to use EFS outline descriptors to analyze the target contour in the infrared image to obtain multi-order features. On this basis, LightGBM is used for training and learning, the best feature subset is selected, and the redundant components are eliminated. SRC is used to classify the outline descriptors of the test sample and obtain its corresponding target category
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