OBJECT CLASSIFICATION IN INFRARED IMAGES USING DEEP REPRESENTATIONS

Date

2016-09-29

Author

Gündoğdu, 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 study, we address the problem of infrared (IR) object classification that divides the object appearance space hierarchically with a binary decision tree structure. Binary decisions are made by using the special features of the object appearances. These features are extracted using a fully connected deep neural network learnt by training samples. At each node of the tree, we train individual deep CNNs such that each node specializes in its corresponding subspace. The proposed classification algorithm is evaluated 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 ship/boat, 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

Infrared, Classification, Thermal targets, Thermal dataset generation, Tree-based classification

URI

https://hdl.handle.net/11511/40905

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar