Weakly Supervised Learning for Target Detection in Remote Sensing Images
Weakly Supervised Learning for Target Detection in Remote Sensing Images
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DOI：
10.1109/lgrs.2014.2358994
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发表时间：
2015-04
影响因子：
4.8
通讯作者：
Dingwen Zhang;Junwei Han;Gong Cheng;Zhenbao Liu;Shuhui Bu;Lei Guo
中科院分区：
文献类型：
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作者：
Dingwen Zhang;Junwei Han;Gong Cheng;Zhenbao Liu;Shuhui Bu;Lei Guo
在这封信中，我们开发了一个新的框架，该框架利用弱监督的学习技术从遥感图像中有效地检测目标，这使我们能够减少收集训练数据的繁琐的手动注释，同时在很大程度上保持检测准确性。所提出的框架包括一个弱监督的训练程序，以产生检测器和一个有效的方案，以检测到测试图像的目标。对具有不同空间分辨率并包含不同类型目标的三个基准测试的全面评估以及与传统监督学习方案的比较证明了拟议框架的效率和有效性。
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.