Hyperspectral image unmixing using autoencoder cascade
Hyperspectral image unmixing using autoencoder cascade
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DOI：
10.1109/whispers.2015.8075378
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
Rui Guo;Wei Wang;H. Qi
中科院分区：
文献类型：
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作者：
Rui Guo;Wei Wang;H. Qi
高光谱图像解混是从高度混合的光谱图像中估计纯源信号（端元）及其比例（丰度）的过程。由于模型的不准确性和观测噪声，混合分解一直是一个非常具有挑战性的问题。在本文中，我们利用自动编码器的潜力来解决解混的挑战。该算法考虑了两个重要因素：第一，高光谱图像中普遍存在观测噪声，对解混结果有很大影响;第二，混合过程中包含稀疏性先验，需要考虑稀疏性先验来辅助端元提取。建议的自编码器级联级联边缘化去噪自编码器和非负稀疏自编码器，以解决解混问题，隐式地对观测数据进行去噪，并采用自适应稀疏约束。该算法进行了测试的一组合成的混合物和一个真实的高光谱图像。实验结果表明，该算法在高噪声环境下的性能优于几种先进的混合分解方法。
Hyperspectral image unmixing is the process of estimating pure source signals (endmemebers) and their proportions (abundances) from highly mixed spectroscopic images. Due to model inaccuracies and observation noise, unmixing has been a very challenging problem. In this paper, we exploit the potential of using autoencoder to tackle the unmixing challenges. Two important facts are considered in the algorithm: first, the observation noise in the hyperspectral image generally exists and largely affects the unmixing results; second, the mixing process contains sparsity priori which should be considered to assist the endmember extraction. The proposed autoencoder cascade concatenates a marginalized denoising autoencoder and a non-negative sparse autoencoder to solve the unmixing problem which implicitly denoises the observation data and employs the self-adaptive sparsity constraint. The algorithm is tested on a set of synthetic mixtures and a real hyperspectral image. The experimental results demonstrate the proposed algorithm outperforms several advanced unmixing approaches in highly noisy environment.