Abstract
Hyperion images from Earth Observing-1 (EO-1) are being used in natural resources assessment and management. The evaluation and verification of Hyperion images for the above applications are validating the EO-1 mission. However, the presence of random and striping noises in Hyperion images affect the accuracy of the results. 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. Thus, a collective approach for correcting pixels with no-data values and removing random noise and stripes from Hyperion radiance images is developed. In the developed method, first, no-data valued pixels are identified and corrected using a local median filter. Minimum noise fraction transformation is then used to reduce random noise from noise-dominated bands. Further, spatial statistical techniques are used to reduce random noise from the rest of the bands. 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. 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 Lulea area, northern Sweden. The results show that the algorithm reduces random and striping noise without introducing unwanted effects and alterations in the original normal data values in the images.
Highlights
Hyperion, a hyperspectral sensor, is on board the National Aeronautics and Space Administration’s Earth Observing-1 (NASA-EO-1) satellite
The above algorithm has been implemented and processed Hyperion images of the study areas using Environment for Visualizing Images” (ENVITM) software and a programming language Interactive Data Language” (IDL) provided by Exelis Visual Information Solutions
The effectiveness of the proposed algorithm and results were compared with the results obtained using some of the previous most widely used algorithms from Hyperion images from the study areas
Summary
A hyperspectral sensor, is on board the National Aeronautics and Space Administration’s Earth Observing-1 (NASA-EO-1) satellite. Hyperion sensor data are the only globally available spaceborne hyperspectral system, which provides hyperspectral Earth exploration data for better-quality characterization of Earth’s surface, for the determination of the composition of surface material for geological applications[1] and natural resources assessments and managements. The distributed Hyperion datasets are radiometrically corrected and orthorectified as level 1A datasets. Level 1A (L1R) datasets are generated from level 0 datasets through the following preprocessing steps: echo correction, smear correction, background removal, bad pixel repair, radiometric correction, and checking the image quality.[2,3] The above corrections, in principle, are expected to recover untrustworthy/noisy pixels, though even a cursory visual inspection of a typical Hyperion L1R image shows this is not the case. Bands close to the water vapor, oxygen, Journal of Applied Remote Sensing
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