Automated Bias-Compensation Approach for Pushbroom Sensor Modeling Using Digital Elevation Model PROJECT TITLE :Automated Bias-Compensation Approach for Pushbroom Sensor Modeling Using Digital Elevation ModelABSTRACT:Bias compensation of rational polynomial coefficients (RPCs) is one of the foremost important preprocessing steps in high-resolution satellite Image Processing. It typically requires correct ground control points (GCPs), however GCP acquisition is both time consuming and laborious. During this paper, we have a tendency to propose a time- and price-economical technique for automated bias compensation of the RPC of high-resolution stereo image pairs. Two Korean Multi-purpose Satellite-two (KOMPSAT-two) stereo image pairs acquired in Daejeon and Busan, Korea, and the Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) with the spatial resolution of three arcsec (∼90 m) were used for analysis. Within the two study areas, 33 and twenty nine check points were respectively used for the performance evaluation. Once bias compensation with the proposed method, the root-mean-square (RMS) errors for both of the study areas were less than ten m, in all coordinate elements, while the RMS error vectors were approximately 10 m. Although the RMS error vectors were slightly larger than the standard deviations of the residual errors of the initial ground coordinates, it would appear that they yielded acceptable values as a result of the proposed method largely depends on the spatial resolution, the error of the SRTM DEM, the tie purpose selection error, and therefore on. Thus, it can be concluded that the proposed technique allows for the automated bias compensation of RPCs of KOMPSAT-2 pictures. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Double-Gated, Spindt-Type Field Emitter With Improved Electron Beam Extraction A Space-Vector PWM-Based Voltage-Balancing Approach With Reduced Current Sensors for Modular Multilevel Converter