Multiple Object Extraction from Aerial Imagery with Convolutional Neural Networks
An automatic system to extract terrestrial objects from aerial imagery has many applications in a wide range of areas. However, in general, this task has been performed by human experts manually, so that it is very costly and time consuming. There have been many attempts at automating this task, but many of the existing works are based on class-specific features and classifiers. In this article, the authors propose a convolutional neural network (CNN)-based building and road extraction system. This takes raw pixel values in aerial imagery as input and outputs predicted three-channel label images (building‐road‐background). Using CNNs, both feature extractors and classifiers are automatically constructed. The authors propose a new technique to train a single CNN efficiently for extracting multiple kinds of objects simultaneously. Finally, they show that the proposed technique improves the prediction performance and surpasses state-of-the-art results tested on a publicly available aerial imagery dataset.
Document Type: Research Article
Publication date: January 1, 2016
This article was made available online on December 14, 2015 as a Fast Track article with title: "Multiple Object Extraction from Aerial Imagery with Convolutional Neural Networks".
The Journal of Imaging Science and Technology (JIST) is dedicated to the advancement of imaging science knowledge, the practical applications of such knowledge, and how imaging science relates to other fields of study. The pages of this journal are open to reports of new theoretical or experimental results, and to comprehensive reviews. Only original manuscripts that have not been previously published, nor currently submitted for publication elsewhere, should be submitted.
IS&T's JIST-first publication option allows authors wishing to present their work at conferences, but have a journal citation for their paper, to submit a paper to JIST that follows the same rigorous peer-review vetting and publication process as traditional JIST articles, but with the benefit of a condensed time-to-publication time frame and guaranteed conference presentation slot.
Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.
- Editorial Board
- Information for Authors
- Submit a Paper
- Subscribe to this Title
- Membership Information
- Information for Advertisers
- Terms & Conditions
- Information for JIST-First Authors
- Ingenta Connect is not responsible for the content or availability of external websites