Subpixel Mapping of Hyperspectral Image Based on Multiscale and Multifeature

The ubiquity of mixed pixels in hyperspectral images makes it difficult for traditional classification techniques to determine the spatial distribution of land-cover classes accurately. Subpixel mapping (SPM) technology is an effective method to solve this problem. Aiming at taking the multiple scales and the spatial features into account, an SPM method based on multiscale and multifeature (MSMF) is proposed, so as to effectively improve the accuracy of SPM. First, the maximum linearization index (MLI) method of the nonredundant complete straight-line (CSL) set is designed to identify the linear distribution feature of land-cover (LC) classes. Then, different methods are applied to different spatial features and unified together finally, where the template matching iterative exchange is used for the linear distribution classes, and the multiscale spatial dependence (MSD) iterative exchange method combined with area perimeter is used for the planar distribution classes. Experiments on three remote sensing images are carried out to evaluate the performance of MSMF. The results show that the proposed method can effectively improve the accuracy of SPM.

Automated summary

This paper proposes a subpixel mapping (SPM) method based on multiscale and multifeature (MSMF) to address the accuracy issue caused by mixed pixels in hyperspectral images. By utilizing the maximum linearization index method and different spatial feature processing methods, the proposed method can effectively improve the accuracy of subpixel mapping.