Image-based estimation and validation of niirs for high-resolution satellite images

As high resolution satellite images are being used widely in many applications more and more users are demanding images of good quality. The 'quality' of satellite images are expressed by many technical terms such as ground sampling distance, modular transfer function, and signal to noise ration and by NIIRS (National Imagery Interpretability Rating Scale) in user community. The purpose of this study is to develop techniques to estimate NIIRS of images through image analysis and using the GIQE (General Image Quality Equation). We assessed NIIRS values by human operators for various high resolution images. We then used GIQE and estimated NIIRS values through image analysis. We compared the NIIRS values obtained through image analysis with the values from human operators and with the NIIRS values provided in the image metadata. Results showed that the NIIRS values provided in the metadata were larger than the values estimated by human operator. This could mean that the value in the metadata assumes ideal conditions and the exact cause of this difference is under current investigation. The NIIRS values estimated through image analysis were lower than the values estimated manually. However, they showed the same pattern as the NIIRS values estimated manually. This indicates that the NIIRS values estimated though image analysis using the GIQE can represent actual interpretability of the image. This also indicates that if we can provide edge points automatically we may achieve fully automatic estimation of NIIRS values. The contribution of this study is that we proved the reliability of image analysis methods for calculating NIIRS values and showed the possibility of an automated technique of estimating NIIRS from images so that the value of NIIRS is systematically calculated at satellite ground stations.