@inproceedings{8b70605c19624aedb1b391e794bba36d,
title = "LW-ODF: A Light-Weight Object Detection Framework for Optical Remote Sensing Imagery",
abstract = "In this paper, we propose to extract the multi-scaled and rotation-insensitive deep features to address the issues of object multi-solutions and rotations in geospatial object detection. To this end, we develop a novel object detection framework where a rotation-insensitive convolution neural network is applied for extracting multi-scaled and direction-insensitive feature representation and then the learned features can be fed into the ensemble classifier learning with fast feature pyramid. Such a non-end-to-end learning strategy intuitively reduces the computational cost without the additional performance loss, yielding an effective and efficient light-weight object detection framework. Experimental results conducted on the NWPU VHR-10 dataset demonstrate that the proposed framework outperforms several state-of-the-art baselines.",
keywords = "Deep learning, direction-insensitive, geospatial object detection, light-weight, multi-scaled, optical remote sensing imagery",
author = "Xin Wu and Danfeng Hong and Pedram Ghamisi and Wei Li and Ran Tao",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",
year = "2019",
month = jul,
doi = "10.1109/IGARSS.2019.8898673",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1462--1465",
booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",
address = "United States",
}