Infrared Object Classification Using Decision Tree Based Deep Neural Networks

Date

2016-05-19

Author

Gundogdu, Erhan
Koç, Aykut
Alatan, Abdullah Aydın

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this work, we focus on the problem of infrared (IR) object classification by dividing the object appearance space hierarchically with a binary decision tree structure. Specially designed features of the object appearances make the binary decisions at each node of the tree. These features are extracted using a fully connected deep neural network. At each node of the tree, we train individual deep CNNs such that each node specializes in its corresponding subspace. The proposed method is tested in our generated dataset, which consists of IR targets collected from different video records obtained from different IR sensors (both midwave and longwave) and taken from real world field. The generated dataset consists of four different class labels as shiplboat, tank, plane and helicopter containing a total of 16K samples. Using the proposed tree-based classifier, we observe a favourable performance increase in our dataset against a single deep CNN classifier.

Subject Keywords

Deep neural networks, Infrared object; classification

URI

https://hdl.handle.net/11511/53395

Conference Name

24th Signal Processing and Communication Application Conference (SIU)

Collections

Graduate School of Natural and Applied Sciences, Conference / Seminar

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Citation Formats

E. Gundogdu, A. Koç, and A. A. Alatan, “Infrared Object Classification Using Decision Tree Based Deep Neural Networks,” presented at the 24th Signal Processing and Communication Application Conference (SIU), Zonguldak, TURKEY, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53395.