Abstract: An end-member extraction method based on segmented VCA, includes: conducting rough segmentation on a hyperspectral image by using an unsupervised classification method to partition image elements having a similar substance into the same block; conducting end-member extraction on an area in each partitioned block by using VCA, inverting the abundance by using a least square method after the end-member extraction, and determining one main end-member for each block according to the abundance value; and extracting the main end-members in all blocks and forming an end-member matrix of a global image. The VCA end-member extraction method is used in relatively simple partitioned environment blocks, and the main end-members in the blocks are then controlled by using the abundance inversion result feedback in the blocks, so as to prevent missing main end-members.

Abstract: The present invention discloses an end-member extraction method based on segmented VCA, comprising: conducting rough segmentation on a hyperspectral image by using an unsupervised classification method to partition image elements having a similar substance into the same block; conducting end-member extraction on an area in each partitioned block by using VCA, inverting the abundance by using a least square method after the end-member extraction, and determining one main end-member for each block according to the abundance value; and extracting the main end-members in all blocks and forming an end-member matrix of a global image. In the present invention, the VCA end-member extraction method is used in relatively simple partitioned environment blocks, and the main end-members in the blocks are then controlled by using the abundance inversion result feedback in the blocks, so as to prevent missing main end-members.