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Recognition of thermal infrared oil tank targets based on blotch detection and clustering and SVM classification
Author(s): Lan Ma; Xiaoyong Chen; Juncai Peng
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Paper Abstract

This paper proposes a method for detecting oil tank targets that combines improved blotch feature detection and SVM sample learning in order to address deficiencies in the recognition of oil tank targets in thermal infrared remote sensing images due to indistinct edge information, high noise level, and small dimensions. First of all, initial detection was conducted on the basis of blotch features and mass features. Then the features of oil tank targets and an optimal combination of classification features were generated from sample learning. Finally oil tank targets were detected through classification based on SVM sample learning. The results of the experiment show that: 1) by setting appropriate parameters, this method combines blotch features and textural features for the extraction of a more comprehensive range of TIR oil tank features that can achieve the effective detection of oil tank targets; 2) based on the detection of blotch targets, this method can filter out false targets with relatively high accuracy and is capable of more stable and efficient recognition of Type #1 and Type #2 targets.

Paper Details

Date Published: 12 March 2019
PDF: 8 pages
Proc. SPIE 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 110230Y (12 March 2019); doi: 10.1117/12.2520936
Show Author Affiliations
Lan Ma, National Univ. of Defense Technology (China)
Xiaoyong Chen, National Univ. of Defense Technology (China)
Juncai Peng, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 11023:
Fifth Symposium on Novel Optoelectronic Detection Technology and Application
Qifeng Yu; Wei Huang; You He, Editor(s)

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