@inproceedings{901032479f01477da7bae5f1e2843b64,
title = "A Weakly-Supervised Deep Network for DSM-Aided Vehicle Detection",
abstract = "With the breakthrough of the spatial resolution of optical remote sensing images at the sub-meter level and the explosive development of deep learning, geospatial object detection has achieved a growing interest in remote sensing community. However, labeling large training datasets in object level is still an expensive and tedious procedure. This might lead to the poor model generalization and degraded network learning ability. To this end, a weakly-supervised deep network (WSDN) is developed for geospatial object detection by applying a digital surface model (DSM)-aided auto-labeling and a pre-trained network learned from the task-independent dataset. Experimental results conducted on the stereo aerial imagery of a large camping site are performed to demonstrate that the proposed WSDN yields better detection results, with 62.78% precision and 55.13% recall.",
keywords = "Deep learning, digital surface model, geospatial object detection, optical remote sensing imagery, vehicle, weakly-supervised",
author = "Xin Wu and Danfeng Hong and Jiaojiao Tian and Ralph Kiefl 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.2019.8897989",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1318--1321",
booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",
address = "United States",
}