MAHUM: A Multitasks Autoencoder Hyperspectral Unmixing Model

Hyperspectral unmixing is a crucial task in hyperspectral image processing and analysis. It aims to decompose mixed pixels into pure spectral signatures and their associated abundances. However, most current unmixing methods ignore the reality that the same pixel of a hyperspectral image has many different reflections simultaneously. To address this issue, we propose a multitask autoencoding model for multiple reflections, which can improve the algorithm's robustness in complex environments. Our proposed framework uses 3-D convolutional neural network (3DCNN)-based networks to jointly learn spectral-spatial priors and adapt to different pixels by complementing the advantages of other unmixing methods. The proposed method can quantitatively evaluate each area of data, which helps improve the algorithm's interpretability. This article presents a multitasks autoencoder hyperspectral unmixing model (MAHUM), which stacks multiple models to deal with various reflections of complex terrain. We also perform sensitivity analysis on some parameters and show experiment results demonstrating our method's ability to express the adaptability of different materials in different methods quantitatively.