TY - GEN
T1 - Object detection with proposals in high-resolution optical remote sensing images
AU - Ding, Huoping
AU - Luo, Qinhan
AU - Zou, Zhengxia
AU - Guo, Cuicui
AU - Shi, Zhenwei
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Detecting object in remote sensing images remains a challenge due to multi-scale objects, complex ground environment and large image size despite of the fast development of machine learning and computer vision technology in recent years. The primary difficulty lies in the fast and accurate location of candidate bounding boxes from a large-size remote sensing image. In this letter, we propose a novel remote sensing object detection method inspired by the recent-popular technique, Object Proposals, to quickly generate high-quality object bounding box locations in remote sensing images. A simple but effective objectness measurement, based on the image gradients and its variants, is proposed. Moreover, to evaluate the effectiveness of our method, we complete the subsequent detection flow based on the convolution neural networks as a standard detection baseline. Experiments show that our method is able to produce high-quality proposals with a desirable computational speed.
AB - Detecting object in remote sensing images remains a challenge due to multi-scale objects, complex ground environment and large image size despite of the fast development of machine learning and computer vision technology in recent years. The primary difficulty lies in the fast and accurate location of candidate bounding boxes from a large-size remote sensing image. In this letter, we propose a novel remote sensing object detection method inspired by the recent-popular technique, Object Proposals, to quickly generate high-quality object bounding box locations in remote sensing images. A simple but effective objectness measurement, based on the image gradients and its variants, is proposed. Moreover, to evaluate the effectiveness of our method, we complete the subsequent detection flow based on the convolution neural networks as a standard detection baseline. Experiments show that our method is able to produce high-quality proposals with a desirable computational speed.
UR - https://www.scopus.com/pages/publications/85034271504
U2 - 10.1007/978-3-319-68935-7_27
DO - 10.1007/978-3-319-68935-7_27
M3 - 会议稿件
AN - SCOPUS:85034271504
SN - 9783319689340
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 242
EP - 250
BT - Intelligent Data Engineering and Automated Learning – IDEAL 2017 - 18th International Conference, Proceedings
A2 - Yin, Hujun
A2 - Zhang, Minling
A2 - Wen, Yimin
A2 - Cai, Guoyong
A2 - Gu, Tianlong
A2 - Tallon-Ballesteros, Antonio J.
A2 - Du, Junping
A2 - Gao, Yang
A2 - Chen, Songcan
PB - Springer Verlag
T2 - 18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017
Y2 - 30 October 2017 through 1 November 2017
ER -