Hyperspectral imaging instruments capture and collect hundreds of different wavelength data corresponding to the same surface. One of the requirements of paramount importance, when dealing with applications that demand swift responses, is the ability to achieve a high real-time performance. In this sense, the authors present a flexible and adaptable FPGA-based solution for extracting the endmembers of a hyperspectral image according to the Modified Vertex Component Analysis (MVCA) algorithm. The proposed approach is capable of adapting its data parallelism by scaling its execution in hardware. The solution uses the dynamic and partial reconfiguration property of FPGAs together with a hardware reconfiguration engine in order to exploit and vary the number of processing elements at run-time.