Restoration and Calibration of Tilting Hyperspectral Super-Resolution Image

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.

ZY-1 02D Hyperspectral Imagery Super-Resolution via Endmember Matrix Constraint Unmixing

“…Hardware-based methods improve the spatial resolution of HSI by directly reducing the pixel size, increasing the number of pixels per unit area by sensor manufacturing techniques [4], or implementing hardware accelerators in field-programmable gate array (FPGA) devices [5][6][7]. Concerning the high cost and limitations of resolution enhancement through hardware-based techniques [8], scholars have undertaken a great deal of research concerning the combination of hardware and software approaches, which generally enhances the HSI spatial resolution by changing the angle of the Charge Coupled Device (CCD) and joint image processing algorithm [9][10][11]. However, these methods can only be used for specific imagery due to the sensor's sampling method.…”