The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 121–127, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-121-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 121–127, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-121-2022
 
30 May 2022
30 May 2022

AUTOMATIC GENERATION OF DEM USING SVM CLASSIFIER

R. Dubey, S. Bharadwaj, and S. Biswas R. Dubey et al.
  • Dept. of Computer Science & Engineering, Rajiv Gandhi Institute of Petroleum Technology Jais, Amethi, Uttar Pradesh, India

Keywords: Classifier, DEM, Extraction, Lidar, Shadow, IOOPL, Noise model

Abstract. People are moving towards an automated strategy for building a Digital Elevation Model (DEM) from a high-resolution Google Earth photograph as technology advances. Manually terrestrial measurements are both time and money costly. The automatic creation of DEMs became possible because to recent advances in image matching algorithms and intelligent filtering. The goal of this work is to give an overview of how to create DEMs automatically in the tough situation of a build-up area with the extraction of building points. The calculation of building heights, is to produce a digital elevation model, which may be easily obtained by collecting automatic Lidar point cloud data The estimation of building height with its shadow plays important role in determination. In this detection of shadow and removal of false shadow is also very important. During picture segmentation, shadow detection is performed. Statistical features are used to extract the suspected shadow. Support vector machine on IOOPL matching could effectively remove the shadow for shadow removal. The approach can successfully detect shadows in high-resolution remote sensing photos of cities and can effectively repair shadows at a rate of over 95%. SVM classifier with the accuracy of 92% as compared to others on lower end. The average variation in height estimation comes out to be 1.64 m. First, the building with height more than 20 m and slope more than 3 m, give significantly improved precision in determining building height. Second, the strategy is cost-effective because most RS images from Google Earth are free to use with less knowledge of RS is needed for computation.