Traditional classification technologies cannot easily or accurately determine the spatial distribution of ground features for hyperspectral images because mixed pixels are widespread throughout the image. Sub-pixel mapping technology is an effective tool to solve this problem. The existing sub-pixel mapping methods that are based on linear optimization encounter two issues in their practical implementation: their inexact objective functions and their excessive computation. This paper proposes a new sub-pixel mapping method to solve the aforementioned problems. The algorithm framework is constructed by combining spectral unmixing with binary particle swarm optimization. The numbers of sub-pixels for each pixel are estimated according to the results of spectral unmixing. The regional perimeter is modified by analyzing the influence on the perimeter and region number as induced by some special cases

such as isolated point or regions that include only two points. The cost function is formulated by considering the regional perimeter and number of connected regions. To reduce the running time of the algorithm

global analysis is replaced with local analysis according to the feature space distribution characteristics

and a new iterative optimization strategy is proposed. Compared with directly minimizing the region circumference based on the image chain code

the modified object function emphasizes the boundary of most regions and does not yield any isolated points or regions that include only two points. The method also improves the recognition rate by more than 2% and the Kappa coefficient by more than 0.05. Moreover

the new iterative optimization strategy nearly halves the CPU time. The experimental results show that the proposed algorithm can improve the mapping accuracy and that the proposed optimization strategies can accelerate the mapping. Given the weak spatial correlation in areas where the end members are uniformly mixed

the proposed algorithm is suitable for hyperspectral images without uniformly mixed areas.