Enhancing the performance of Multiple Endmember Spectral Mixture Analysis (MESMA) for urban land cover mapping using airborne lidar data and band selection

Abstract

Multiple Endmember Spectral Mixture Analysis (MESMA) is a widely applied tool to retrieve spatially explicit information on urban land cover from both hyperspectral and multispectral data, but is still prone to misclassification errors when faced with high inter-class similarity, typical of the complex urban environment. In this study we assessed multiple ways to minimize spectral confusion using airborne lidar data as an additional data source and spectral feature selection. Several approaches were tested using simulated hyperspectral data and two case studies in the city of Brussels, Belgium, one based on hyperspectral (APEX) data and one on multispectral (Sentinel-2) data. We found that the implementation of height distribution information (1) as an endmember model selection tool and (2) as a basis for additional fraction constraints at the individual pixel scale, significantly reduced spectral confusion between spectrally similar, but structurally different land cover classes (on average by 80% for the APEX case). This had a net positive effect on subpixel fraction estimations (average R2 increased from 0.34 to 0.80 and from 0.23 to 0.63 for APEX and Sentinel-2, respectively) and pixel classification accuracies (kappa increased from 0.38 to 0.6 for the APEX case). When applied to fine spatial resolution data containing many single-class pixels, endmember model selection based on height information resulted in the additional benefit of lowering computation times by 85%. Spectral feature selection successfully discarded redundant spectral information (on average retaining only 19 out of 218 bands), thereby further lowering processing times by 50%, without affecting accuracies. Despite these significant improvements, spectral confusion remained an issue between classes showing no distinction in height information, particularly pavement and soil. Future research should therefore focus on integrating the proposed approach with advanced endmember detection and selection algorithms, along with exploring innovative ways of highlighting small spectral differences using spectral transformations. The algorithm we propose constitutes a viable approach for mapping of structurally diverse ecosystems, such as urban environments, at multiple spatial scales and with varying level of thematic detail.