List<BibtexEntry> entries = importer.importEntries(IsiImporterTest.class
.getResourceAsStream("IEEEImport1.txt"));
assertEquals(1, entries.size());
BibtexEntry a = entries.get(0);
assertEquals(a.getType().getName(), BibtexEntryType.ARTICLE, a.getType());
assertEquals("Geoscience and Remote Sensing Letters, IEEE", a.getField("journal"));
assertEquals(
"Improving Urban Road Extraction in High-Resolution " +
"Images Exploiting Directional Filtering, Perceptual " +
"Grouping, and Simple Topological Concepts",
a.getField("title"));
assertEquals("4", a.getField("volume"));
assertEquals("3", a.getField("number"));
assertEquals("1545-598X", a.getField("SN"));
assertEquals("387--391", a.getField("pages"));
assertEquals("Gamba, P. and Dell'Acqua, F. and Lisini, G.", a.getField("author"));
assertEquals("2006", a.getField("year"));
assertEquals("Perceptual grouping, street extraction, urban remote sensing", a.getField("keywords"));
assertEquals("In this letter, the problem of detecting urban road " +
"networks from high-resolution optical/synthetic aperture " +
"radar (SAR) images is addressed. To this end, this letter " +
"exploits a priori knowledge about road direction " +
"distribution in urban areas. In particular, this letter " +
"presents an adaptive filtering procedure able to capture the " +
"predominant directions of these roads and enhance the " +
"extraction results. After road element extraction, to both " +
"discard redundant segments and avoid gaps, a special " +
"perceptual grouping algorithm is devised, exploiting " +
"colinearity as well as proximity concepts. Finally, the road " +
"network topology is considered, checking for road " +
"intersections and regularizing the overall patterns using " +
"these focal points. The proposed procedure was tested on a " +
"pair of very high resolution images, one from an optical " +
"sensor and one from a SAR sensor. The experiments show an " +
"increase in both the completeness and the quality indexes " +
"for the extracted road network.", a.getField("abstract"));
}