A web-based semi-automated method for semantic annotation of high schools in remote sensing images. You, M. C., Sun, Z., Di, L., & Guo, Z. In 2014 The Third International Conference on Agro-Geoinformatics, pages 1–4, August, 2014. doi abstract bibtex The overwhelming volume of routine image acquisition requires automated methods or systems for feature discovery instead of manual image interpretation. While most existing researches focus on extracting elementary features such as basic terrains and individual objects, the detection of compound feature is still a challenge. This paper proposes a semi-automated approach integrating supervised image classification and geo-processing workflow to discover and annotate compound objects within RS images. Taking the high school in U.S. as an example, we developed a web-based prototype system to detect compound objects. Experimental results by the prototype show that the approach is capable of annotating high schools with an acceptable accuracy. This paper demonstrates a novel way to leverage existing technologies in completing the semantic annotation of RS images.
@inproceedings{you_web-based_2014,
title = {A web-based semi-automated method for semantic annotation of high schools in remote sensing images},
doi = {10.1109/Agro-Geoinformatics.2014.6910672},
abstract = {The overwhelming volume of routine image acquisition requires automated methods or systems for feature discovery instead of manual image interpretation. While most existing researches focus on extracting elementary features such as basic terrains and individual objects, the detection of compound feature is still a challenge. This paper proposes a semi-automated approach integrating supervised image classification and geo-processing workflow to discover and annotate compound objects within RS images. Taking the high school in U.S. as an example, we developed a web-based prototype system to detect compound objects. Experimental results by the prototype show that the approach is capable of annotating high schools with an acceptable accuracy. This paper demonstrates a novel way to leverage existing technologies in completing the semantic annotation of RS images.},
booktitle = {2014 {The} {Third} {International} {Conference} on {Agro}-{Geoinformatics}},
author = {You, M. C. and Sun, Z. and Di, L. and Guo, Z.},
month = aug,
year = {2014},
keywords = {Accuracy, Compound geospatial object, compound object detection, Compounds, Educational institutions, feature discovery, Feature extraction, geo-processing workflow, geophysical image processing, Geospatial analysis, high schools, image acquisition, image classification, manual image interpretation, object detection, remote sensing, Remote sensing, remote sensing images, RS images, semantic annotation, semantic Web, Semantics, supervised image classification, Web-based semi-automated method},
pages = {1--4},
file = {IEEE Xplore Abstract Record:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/HYBJNGE2/6910672.html:text/html;IEEE Xplore Full Text PDF:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/ETQJB5N2/You et al. - 2014 - A web-based semi-automated method for semantic ann.pdf:application/pdf}
}
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