Hyperspectral Unmixing with AutoEncoder Network in Wavelet Domain

Hyperspectral unmixing is an important task in hyperspectral applications. Its essence is to estimate the spectra (endmembers) and corresponding proportion (abundances) of pure substances. In this paper, we propose a new hyperspectral unmixing method with autoencoder network in wavelet domain. Based on the sparsity of wavelet coefficients, the high frequency parts are truncated to provide more reliable spectral similarity. After that, the batch normalization and ReLU function are followed to construct the hidden layer. In terms of loss function, the l(2) and l(1) norm are added to low and high frequency coefficients to ensure the energy fidelity and enhance the sparsity, respectively. Moreover, the hidden layer is characterized by l(1/2) norm to model the sparse prior of abundance, and SAD is used to enhance the spectral similarity. A large number of experiments show that the proposed method is superior to the most advanced methods.