#A bad case of stripes images verification#

The effectiveness and efficiency of the algorithm is demonstrated by application to two Hyperion images: one from the Udaipur area, western India, and another from the Luleå area, northern Sweden. Finally, a local quadratic regression by a least squares method is used to correct bad columns and global stripes, and a local-spatial-statistics-based algorithm is used to detect and correct local stripes. Further, spatial statistical techniques are used to reduce random noise from the rest of the bands. Minimum noise fraction transformation is then used to reduce random noise from noise-dominated bands. In the developed method, first, no-data valued pixels are identified and corrected using a local median filter. Thus, a collective approach for correcting pixels with no-data values and removing random noise and stripes from Hyperion radiance images is developed. Therefore, reduction of random noise and stripes from Hyperion images becomes indispensable for the evaluation of the results in natural resources assessment and in optimum use of the data. However, the presence of random and striping noises in Hyperion images affect the accuracy of the results.

#A bad case of stripes images verification#

The evaluation and verification of Hyperion images for the above applications are validating the EO-1 mission. Hyperion images from Earth Observing-1 (EO-1) are being used in natural resources assessment and management.