Abstract
Satellite images have been widely used to produce land use and land cover maps and to generate other thematic layers through image processing. However, images acquired by sensors onboard various satellite platforms are affected by a systematic sensor and platform-induced geometry errors, which introduce terrain distortions, especially when the sensor does not point directly at the nadir location of the sensor. To this extent, an automated processing chain of WorldView-3 image orthorectification is presented using rational polynomial coefficient (RPC) model and laser scanning data. The research is aimed at analyzing the effects of varying resolution of the digital surface model (DSM) derived from high-resolution laser scanning data, with a novel orthorectification model. The proposed method is validated on actual data in an urban environment with complex structures. This research suggests that a DSM of 0.31 m spatial resolution is optimum to achieve practical results (root-mean-square error = 0.69 m ) and decreasing the spatial resolution to 20 m leads to poor results (root-mean-square error = 7.17 ). Moreover, orthorectifying WorldView-3 images with freely available digital elevation models from Shuttle Radar Topography Mission (SRTM) (30 m) can result in an RMSE of 7.94 m without correcting the distortions in the building. This research can improve the understanding of appropriate image processing and improve the classification for feature extraction in urban areas.
Highlights
Increased availability of high-resolution satellite images is driving the rapid expansion in remote sensing applications, including commercial, industrial, governmental, and research domains [1,2,3,4,5,6,7,8,9,10]
We focused on the distortion building which the number of the distortion building is 43 in this study, so we added the ground control points (GCPs) on these buildings as shown in Figure 5 (2) Rectification model with rational polynomial coefficient (RPC): Grodecki and Dial [20] reported that the RPC model is developed based on GCPs and digital elevation model (DEM) data to orthorectify images
Light Detection and Ranging (LiDAR) data were used to obtain high-ability GCPs and digital surface model (DSM) at the increased accuracy required for photogrammetry and orthorectification
Summary
Increased availability of high-resolution satellite images is driving the rapid expansion in remote sensing applications, including commercial, industrial, governmental, and research domains [1,2,3,4,5,6,7,8,9,10]. High-resolution satellite images are commonly used in urban remote sensing applications, such as change detection, urban sprawl, land use/land cover mapping, environmental studies, and transportation [5,6,7]. Joshi et al [2] and Peng et al [6] suggested that using elevation data or multiple images acquired from different angles in remote sensing applications, such as image classification, building detection, and city modeling, is preferable. These problems originated from the reduced pixel dimensions and off-nadir viewing. One approach to correct such geometric errors in satellite images is called orthorectification, which is the adjustment of a perspective image geometrically to an orthogonal image by transforming the coordinates from an image to the ground spaces and removing relief displacements and tilt
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