Proportion Estimation of Urban Mixed Scenes Based on Nonnegative Matrix Factorization for High Spatial Resolution Remote Sensing Images

Abstract

Urban scenes play a pivotal role in urban planning and environmental protection. Thus, depicting urban scenes is an increasingly important research area in remote sensing applications. Researchers have not treated quantitative measurements on scenes in much detail. So far, there has been little discussion about scene decomposition methods for high resolution imagery. This article proposed a framework based on nonnegative matrix factorization of mixed scene (NMFMS) to estimate the mixing ratio of urban scenes. The framework consists of two steps, namely deep feature representation and scene decomposition. The urban mixed scenes can be described by the proportions of each sub-scenes. To confirm the feasibility of the proposed method, experiments with high resolution imagery of Hanyang District in Wuhan and a mixed scene data set are applied. We further utilized root-mean-square error (RMSE) and Vector angular distance (VAD) to quantitatively evaluate the effect of scene decomposition. The experimental results confirmed the high precision of the proposed algorithm.