Weakly Supervised Learning for Target Detection in Remote Sensing Images

In this letter, we develop a novel framework of leveraging weakly supervised learning techniques to efficiently detect targets from remote sensing images, which enables us to reduce the tedious manual annotation for collecting training data while maintaining the detection accuracy to large extent. T...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2015-04, Vol.12 (4), p.701-705
Main Authors: Dingwen Zhang, Junwei Han, Gong Cheng, Zhenbao Liu, Shuhui Bu, Lei Guo
Format: Article
Language:English
Subjects:
Detectors
Earth
Feature extraction
Object detection
Remote sensing
Remote sensing image (RSI)
Supervised learning
target detection
Training
weakly supervised learning (WSL)
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Summary:In this letter, we develop a novel framework of leveraging weakly supervised learning techniques to efficiently detect targets from remote sensing images, which enables us to reduce the tedious manual annotation for collecting training data while maintaining the detection accuracy to large extent. The proposed framework consists of a weakly supervised training procedure to yield the detectors and an effective scheme to detect targets from testing images. Comprehensive evaluations on three benchmarks which have different spatial resolutions and contain different types of targets as well as the comparisons with traditional supervised learning schemes demonstrate the efficiency and effectiveness of the proposed framework.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2014.2358994