Shading aware DSM generation from high resolution multi-view satellite images

ABSTRACT

In many cases, the Digital Surface Models (DSMs) and Digital Elevation Models (DEMs) are obtained with Light Detection and Ranging (LiDAR) or stereo matching. As an active method, LiDAR is very accurate but expensive, thus often limiting its use in small-scale acquisition. Stereo matching is suitable for large-scale acquisition of terrain information as the increase of satellite stereo sensors. However, underperformance of stereo matching easily occurs in textureless areas. Accordingly, this study proposed a Shading Aware DSM GEneration Method (SADGE) with high resolution multi-view satellite images. Considering the complementarity of stereo matching and Shape from Shading (SfS), SADGE combines the advantage of stereo matching and SfS technique. First, an improved Semi-Global Matching (SGM) technique is used to generate an initial surface expressed by a DSM; then, it is refined by optimizing the objective function which modeled the imaging process with the illumination, surface albedo, and normal object surface. Different from the existing shading-based DEM refinement or generation method, no information about the illumination or the viewing angle is needed while concave/convex ambiguity can be avoided as multi-view images are utilized. Experiments with ZiYuan-3 and GaoFen-7 images show that the proposed method can generate higher accuracy DSM (12.5–56.3% improvement) with sound overall shape and temporarily detailed surface compared with a software solution (SURE) for multi-view stereo.

KEYWORDS:

• Shape from Shading (SfS)
• multi-view stereo
• Digital Surface Model (DSM)
• high resolution multi-view satellite images

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The Sainte-Maxime dataset used in the study is available from https://www.isprs.org/data/zy-3/Default-HongKong-StMaxime.aspx, other datasets are available from China Center For Resources Satellite Data and Application (CRESDA), http://www.cresda.com/CN/index.shtml, but restrictions apply to the availability of these data, which were used under license for this study.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 41801390] and the National Key R&D Program of China [grant number 2018YFD1100405].

Notes on contributors

Zhihua Hu

Zhihua Hu received the PhD degree in photogrammetry and remote sensing from Wuhan University in 2020. His current research interests include mesh refinement, and multi-view images 3D reconstruction.

Pengjie Tao

Pengjie Tao is currently an associate research fellow. His research interests include photogrammetry, registration of optical images and LiDAR points, and multi-view images 3D reconstruction.

Xiaoxiang Long

Xiaoxiang Long is currently a research fellow. His research interests include satellite photogrammetry, and multi-view images 3D reconstruction.

Haiyan Wang

Haiyan Wang is currently an engineer in the field of satellite mapping. His research interests include satellite photogrammetry, and multi-view images 3D reconstruction.