A Supervised Strategy for Object-Based Spectral Unmixing

Spectral unmixing is a technique to predict the proportions of land cover classes within mixed pixels. The proportions can serve as inputs for land cover classification at finer spatial resolutions (i.e., by subpixel mapping SPM). To achieve high-quality finer spatial resolution land cover mapping, during the spectral unmixing process, it is necessary to accurately identify land cover boundaries, which are typically represented by mixed pixels. The recently proposed unsupervised object-based subpixel mapping model (UO-SPM) incorporates object-level information into spectral unmixing. However, its performance is limited by uncertainties in unsupervised clustering and the automatic threshold segmentation. In this paper, we proposed a supervised strategy for object-based spectral unmixing (SOSU). SOSU segments observed images (i.e., Landsat images in this paper) into objects and uses supervised classifier to produce object-level classification maps. Mixed (i.e., located at object boundaries) and pure pixels (i.e., within object) are subsequently identified using an erosion algorithm applied to the classification maps. The identified mixed pixels are unmixed using the extracted pure pixels as endmembers. The SOSU method not only inherits the advantage of UO-SPM but also exploits supervised and object-based spatial information to achieve more reliable segmentation and unmixing. The effectiveness of SOSU was validated across seven different experimental regions. By introducing supervised information, SOSU increases the correlation coefficient by 0.0347 and reduces the root mean square error and mean absolute error by 0.0352 and 0.0200, respectively, compared with UO (i.e., the spectral unmixing component of UO-SPM).