Introduction - If you have any usage issues, please Google them yourself
Compared with the single band, hyperspectral remote sensing and traditional multi spectral data, a substantial increase in the amount of band width of band, greatly reduced, measure spectral characteristics of ground objects in greater detail, but the increase will inevitably lead to a sharp increase in band data and increase the amount of calculation, and increase the information redundancy of statistical parameter estimation error. So it has important significance to reduce the dimensionality of hyperspectral data. On the one hand, the dimension reduction can make the image far away from noise and improve the quality of image data; on the other hand, it can remove the non value bands in the image, reduce the number of bands, thereby reducing the amount of calculation and improving the efficiency of the calculation. Principal component analysis is used for dimensionality reduction of hyperspectral data processing methods.