The invention belongs to the field of remote sensing image processing, and discloses a hyperspectral image dimension reduction method and device based on self-adaptive collaborative graph discriminantanalysis. The method comprises the steps of selecting part of pixels from original hyperspectral data to serve as training samples; establishing a Tikhonov regular weight coefficient matrix partitioned by categories, and constructing a collaborative representation graph; and through generalized eigenvalue decomposition, obtaining an optimal projection matrix P under an optimization criterion, andprojecting a test sample to a low-dimensional space to realize dimension reduction of the hyperspectral data. According to the method, distance-weighted Tikhonov regularization is coupled with a representation method based on l2-norm minimization, data is projected to a low-dimensional popular space, and collaborative representation characteristics are obtained through l2-norm. In the process ofconstructing the graph, the internal relation between intra-class pixels is fully mined, and cooperative representation is adaptively adjusted through distance weighting measurement. In addition, thegraph weight matrix adopts a block diagonal structure design, so that the calculation cost is reduced, and the discrimination capability is further improved.
Hyperspectral image dimension reduction method and device based on self-adaptive collaborative graph discriminant analysis