Automatic Seamline Network Generation for Urban Orthophoto Mosaicking with the Use of a Digital Surface Model

Abstract

Intelligent seamline selection for image mosaicking is an area of active research in the fields of massive data processing, computer vision, photogrammetry and remote sensing. In mosaicking applications for digital orthophoto maps (DOMs), the visual transition in mosaics is mainly caused by differences in positioning accuracy, image tone and relief displacement of high ground objects between overlapping DOMs. Among these three factors, relief displacement, which prevents the seamless mosaicking of images, is relatively more difficult to address. To minimize visual discontinuities, many optimization algorithms have been studied for the automatic selection of seamlines to avoid high ground objects. Thus, a new automatic seamline selection algorithm using a digital surface model (DSM) is proposed. The main idea of this algorithm is to guide a seamline toward a low area on the basis of the elevation information in a DSM. Given that the elevation of a DSM is not completely synchronous with a DOM, a new model, called the orthoimage elevation synchronous model (OESM), is derived and introduced. OESM can accurately reflect the elevation information for each DOM unit. Through the morphological processing of the OESM data in the overlapping area, an initial path network is obtained for seamline selection. Subsequently, a cost function is defined on the basis of several measurements, and Dijkstra's algorithm is adopted to determine the least-cost path from the initial network. Finally, the proposed algorithm is employed for automatic seamline network construction; the effective mosaic polygon of each image is determined, and a seamless mosaic is generated. The experiments with three different datasets indicate that the proposed method OPEN ACCESS Remote Sens. 2014, 6 12335 meets the requirements for seamline network construction. In comparative trials, the generated seamlines pass through fewer ground objects with low time consumption.

Sliver Removal in Object-Based Change Detection from VHR Satellite Images

“…The reasonable seamline should avoid crossing obvious objects as much as possible, e.g., buildings and high bridges. Relief displacement mainly occurs because a DTM does not contain elevations for these obvious objects (Pan et al, 2014b;Chen et al, 2014). In Figure 4, the proposed method successfully avoided crossing most of the obvious objects, and the seamlines were mainly along the roads or low bridges.…”

Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts

“…Second, the superpixel segmentation can be introduced to greatly improve the optimization efficiency by decreasing the number of elements in graph cuts. Last but not least, the seamline network optimization framework [20,39,41] can be combined with our approach to produce a complete image mosaic from a large set of images automatically.…”

Automatic Mosaicking of Satellite Imagery Considering the Clouds

“…It is an important part of remote sensing imagery production (Pan, 2015;Cresson, 2015;Chen, 2014a). However, the existence of clouds is a problem that cannot be ignored.…”