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 Luleå 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.
Hyperspectral sensors offer narrow spectral bandwidth facilitating better discrimination of various ground materials. However, high spectral resolutions of these sensors result in larger data volumes, and thus pose computation challenges. The increased computational complexity limit the use of hyperspectral data, where applications demands moderate accuracies but economy of processing and execution time. Also the high dimensionality of the feature space adversely affect classification accuracies when the number of training samples is limited – a consequence of Hughes’ effect. A reduction in the number of dimensions lead to the Hughes effect, thus improving classification accuracies. Dimensionality reduction can be accomplished by: (i) feature selection, that is, selection of sub-optimal subset of the original set of features and (ii) feature extraction, that is, projection of the original feature space into a lower dimensional subspace that preserves most of Information. In this contribution, we propose a novel method of feature section by identifying and selecting the optimal bands based on spectral decorrelation using a local curve fitting technique. The technique is implemented on the Hyperion data of a study area from Western India. The results shows that the proposed algorithm is efficient and effective in preserving the useful original information for better classification with reduced data size and dimension.