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Accuracy assessment of remote sensed classified images is considered the backbone of remote sensing image processing to be considered credible. However, reference data to perform this task is also a considerable challenge for the remote sensing analyst. This study was carried out over Kigali city using Landsat remotely sensed imagery acquired on July 15, 2015, to compare multi-sourced reference data performance to assess the accuracy of classified Landsat remote sensed imagery. To achieve this objective, GeoEye-1, WorldView-2, Google earth high-resolution image, and GIS layers have been used to verify the accuracy of remote-sensed data classification.   In this study, we applied different reference data sources to Landsat 2015 classified images to assess the accuracy. Therefore, results from GEOEYE-1 image as reference data source displayed the total accuracy and kappa coefficient of 98.5% and 0.98 respectively. WorldView-2 MS Image revealed 97.25% of total accuracy and a 0.96 Kappa coefficient agreement.High-resolution rectified images generated using El-Shayal Smart GIS Editor also show its capabilities to assess the accuracy of Landsat remote sensed  data whose results were 94% and 0.92%, respectively, for overall accuracy and total Kappa statistics. Furthermore, the remote sensing analyst should not worry about where or how to find reference data to assess image classification so long as they possess GIS shape files. GIS shape files provide good results where the overall accuracy was 92% and a Kappa coefficient of 0.90. Moreover, GIS shape files results showed a slightly lower accuracy because of data properties; it is recommended to check projection before using any spatial data. This paper strongly focused on soft features during ground reference data collection. Test data from GEOEYE-1 images have shown the best thematic accuracy after being overlaid with Kigali 2015 thematic map. All of the referenced data sources, in general, showed the ability to assess remote sensed classified map in the range of 90% to 98.5% for both total accuracies of the map and kappa accuracy

zenodo (cern european organization for nuclear research)

Comparative Performance of Multi-Source Reference Data to Assess the Accuracy of Classified Remotely Sensed Imagery: Example of Landsat 8 OLI Across Kigali City-Rwanda 2015