Abstract
Tilting sampling is a novel sampling mode for achieving a higher resolution of hyperspectral imagery. However, most studies on the tilting image have only focused on a single band, which loses the features of hyperspectral imagery. This study focuses on the restoration of tilting hyperspectral imagery and the practicality of its results. First, we reduced the huge data of tilting hyperspectral imagery by the p-value sparse matrix band selection method (pSMBS). Then, we restored the reduced imagery by optimal reciprocal cell combined modulation transfer function (MTF) method. Next, we built the relationship between the restored tilting image and the original normal image. We employed the least square method to solve the calibration equation for each band. Finally, the calibrated tilting image and original normal image were both classified by the unsupervised classification method (K-means) to confirm the practicality of calibrated tilting images in remote sensing applications. The results of classification demonstrate the optimal reciprocal cell combined MTF method can effectively restore the tilting image and the calibrated tiling image can be used in remote sensing applications. The restored and calibrated tilting image has a higher resolution and better spectral fidelity.
Highlights
Tilting sampling is a novel sampling method that can improve the spatial resolution of an image by changing the imaging angle between a charge-coupled device (CCD) line array in a sensor and the sensor’s moving direction [1,2]
This study focused on building the model between the restored tilting image and the original normal image and solved the calibrated by the least square method
The results showed that the classification accuracy of the calibrated tilting image has not declined, which means that the proposed calibrated method can ensure its result can be used in the remote sensing application
Summary
Tilting sampling is a novel sampling method that can improve the spatial resolution of an image by changing the imaging angle between a charge-coupled device (CCD) line array in a sensor and the sensor’s moving direction [1,2]. Since the tilting sampling method was proposed, many scholars have focused on the research about the imaging angle and the restoration methods of tiling image. It has been found the aliasing in the tilting image is different from that in the normal image, which can be used to improve the details of the tilting image [5].
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