Weakly Supervised Learning for Target Detection in Remote Sensing Images

Zhang D.; Han J.; Cheng G.; Liu Z.; Bu S.; Guo L.

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Weakly Supervised Learning for Target Detection in Remote Sensing Images

机译：用于遥感图像目标检测的弱监督学习

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摘要

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.

机译：在这封信中，我们开发了一个利用弱监督学习技术来有效地从遥感图像中检测目标的新颖框架，这使我们能够减少繁琐的人工注释来收集训练数据，同时在很大程度上保持检测精度。拟议的框架包括一个弱监督训练程序，以产生检测器和一个有效的方案，以从测试图像中检测目标。对具有不同空间分辨率，包含不同类型目标的三个基准进行的全面评估以及与传统监督学习方案的比较证明了所提出框架的效率和有效性。

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《Geoscience and Remote Sensing Letters, IEEE》 |2015年第4期|701-705|共5页
作者
Zhang D.; Han J.; Cheng G.; Liu Z.; Bu S.; Guo L.;
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正文语种 eng
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关键词
Detectors; Earth; Feature extraction; Object detection; Remote sensing; Supervised learning; Training; Remote sensing image (RSI); target detection; weakly supervised learning (WSL);

机译：探测器;地球;特征提取;目标检测;遥感;监督学习;训练;遥感图像（RSI）;目标检测;弱监督学习（WSL）;

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专利

1. Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning [J] . Han J., Zhang D., Cheng G., Geoscience and Remote Sensing, IEEE Transactions on . 2015,第6期

机译：基于弱监督学习和高级特征学习的光学遥感图像目标检测
2. Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping [J] . Zhou Peicheng, Cheng Gong, Liu Zhenbao, Multidimensional systems and signal processing . 2016,第4期

机译：基于转移的深度特征和负引导的弱监督目标检测
3. Simple weakly supervised deep learning pipeline for detecting individual red-attacked trees in VHR remote sensing images [J] . Qiao Rui, Ghodsi Ali, Wu Honggan, Remote sensing letters . 2020,第7a9期

机译：用于在VHR遥感图像中检测单个红攻击树的简单弱监督的深度学习管道
4. Negative Bootstrapping for Weakly Supervised Target Detection in Remote Sensing Images [C] . Peicheng Zhou, Dingwen Zhang, Gong Cheng, International Conference on Multimedia Big Data . 2015

机译：负引导，用于遥感图像中的弱监督目标检测
5. Unsupervised and generalized orthogonal subspace projection and constrained energy minimization for target detection and classfication in remotely sensed imagery. [D] . Ren, Hsuan. 2000

机译：用于遥感影像中目标检测和分类的无监督广义正交子空间投影和约束能量最小化。
6. A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images [O] . Bin Hou, Yunhong Wang, Qingjie Liu 2016

机译：基于显着性的半监督建筑物变化检测方法的高分辨率遥感影像
7. Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning [O] . Han, Junwei, Zhang, Dingwen, Cheng, Gong, 2015

机译：基于弱监督学习和高级特征学习的光学遥感图像目标检测

Weakly Supervised Learning for Target Detection in Remote Sensing Images

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