| 摘要 |
<p>A method of clustering and reducing hyperspectral image data having a plurality of spatial pixels, and a plurality of spectral dimensions associated with each spatial pixel, includes computing an initial basis vector associated with the hyperspectral image data, unmixing the initial basis vector with the hyperspectral image data to generate an initial set of coefficients and an associated set of residual vectors, generating a set of clusters based on the initial set of coefficients, and iteratively computing one or more additional basis vectors and updating the set of clusters. The iterative computing includes calculating a subsequent basis vector based on a residual vector associated with a prior unmixing, unmixing the subsequent basis vector with a prior set of residual vectors to generate additional coefficients associated with each pixel, and iteratively computing cluster centers and content including an additional dimension associated with the subsequent basis vector.</p> |
