The sizes of BLRs and BH masses of double-peaked broad low-ionization emission line objects

In this paper, the sizes of the BLRs and BH masses of DouBle-Peaked broad low-ionization emission line emitters (dbp emitters) are compared using different methods: virial BH masses vs BH masses from stellar velocity dispersions, the size of BLRs from the continuum luminosity vs the size of BLRs from the accretion disk model. First, the virial BH masses of dbp emitters estimated by the continumm luminosity and line width of broad H$\beta$ are about six times (a much larger value, if including another dbp emitters, of which the stellar velocity dispersions are traced by the line widths of narrow emission lines) larger than the BH masses estimated from the relation $M_{BH} - \sigma$ which is a more accurate relation to estimate BH masses. Second, the sizes of the BLRs of dbp emitters estimated by the empirical relation of $R_{BLR} - L_{5100\AA}$ are about three times (a much larger value, if including another dbp emitters, of which the stellar velocity dispersions are traced by the line widths of narrow emission lines) larger than the mean flux-weighted sizes of BLRs of dbp emitters estimated by the accretion disk model. The higher electron density of BLRs of dbp emitters would be the main reason which leads to smaller size of BLRs than the predicted value from the continuum luminosity.Comment: 7 pages, two figures and one table. Accepted by MNRA

GeoSay: A Geometric Saliency for Extracting Buildings in Remote Sensing Images

Automatic extraction of buildings in remote sensing images is an important but challenging task and finds many applications in different fields such as urban planning, navigation and so on. This paper addresses the problem of buildings extraction in very high-spatial-resolution (VHSR) remote sensing (RS) images, whose spatial resolution is often up to half meters and provides rich information about buildings. Based on the observation that buildings in VHSR-RS images are always more distinguishable in geometry than in texture or spectral domain, this paper proposes a geometric building index (GBI) for accurate building extraction, by computing the geometric saliency from VHSR-RS images. More precisely, given an image, the geometric saliency is derived from a mid-level geometric representations based on meaningful junctions that can locally describe geometrical structures of images. The resulting GBI is finally measured by integrating the derived geometric saliency of buildings. Experiments on three public and commonly used datasets demonstrate that the proposed GBI achieves the state-of-the-art performance and shows impressive generalization capability. Additionally, GBI preserves both the exact position and accurate shape of single buildings compared to existing methods