Realistic Mixing Miniature Scene Hyperspectral Unmixing: From Benchmark Datasets to Autonomous Unmixing

Abstract

Mixed pixels that contain more than one material type are common in mid/low spatial resolution remote sensing imagery. Hyperspectral unmixing is aimed at decomposing the mixed pixels into endmembers and abundances. However, there are few datasets that are suitable for quantitatively evaluating unmixing accuracies, and the ground-truth abundances of the existing datasets are often generated in an approximate way. To address the lack of real unmixing datasets for quantitative evaluation, we built the realistic mixing miniature scenes (RMMS) dataset, which can be used to quantitatively evaluate the unmixing accuracy of different algorithms. The RMMS dataset consists of a simple mixture scene with homogeneous flat materials and a complex mixture scene with 3-D structural features. The features of the RMMS dataset also take point, line, and polygon characteristics into consideration, and the spectral similarity of the materials increases the challenge of the spectral unmixing. In the RMMS dataset, due to the multiscale observation characteristics of the spatiotemporal scanning modality, it can avoid the registration error between RGB and hyperspectral data, and it can ensure that the endmembers are pure pixels. Most of the autonomous hyperspectral unmixing algorithms focus on solving some of the unmixing problems and have difficulty achieving fully autonomous hyperspectral unmixing (FAHU). In this article, to overcome this shortcoming, a fully autonomous hyperspectral unmixing method called FAHU is proposed to take advantage of the spatial information. Some of the state-of-the-art autonomous hyperspectral unmixing algorithms are used to evaluate the performance with the RMMS dataset, and the experimental results show the advantages and disadvantages of the different autonomous unmixing algorithms.