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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Blind Hyperspectral Unmixing using Dual Branch Deep Autoencoder with Orthogonal Sparse Prior
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Blind Hyperspectral Unmixing using Dual Branch Deep Autoencoder with Orthogonal Sparse Prior

机译：使用双分支深度自动化器具有正交稀疏的盲目光谱解密

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摘要

Blind hyperspectral unmixing has become an important task for hyperspectral applications. In this paper, we propose a dual branch autoencoder with a novel sparse prior to simultaneously extract endmembers and abundances from the raw HSI. The dual branch structure extends the linear mixing model by only modeling linear mixtures of the endmembers and treating the bilinear interactions as error. In this way, the proposed model doesn't require the assumptions of explicit forms of bilinear interactions. The proposed sparse prior, named as orthogonal sparse prior, is based on the key observation that the abundance vector of one pixel is very sparse, there are often no more than two non-zero elements. Different from the conventional norm-based sparse prior which assumes the abundance maps are independent, the orthogonal sparse prior explores the orthogonality between the abundance maps. Extensive experiments on two real datasets show that the proposed method significantly and consistently outperforms the compared state-of-the-art methods, with up to 50% improvements.
机译：盲眼光谱解密已成为高光谱应用的重要任务。在本文中，我们提出了一种双分支自动拓，具有新颖的稀疏，同时提取了原始HSI的终端和丰富。双分支结构仅通过仅建模终端的线性混合物来扩展线性混合模型，并将双线性相互作用视为误差。以这种方式，所提出的模型不需要明确形式的双线性相互作用的假设。所提出的稀疏先前，名为正交稀疏的先前，基于一个像素的丰度向量非常稀疏，通常不超过两个非零元素。与假设丰度图的传统规范的稀疏性不同，正交稀疏先前探讨了丰富地图之间的正交性。在两个真实数据集上进行广泛的实验表明，该方法显着且始终如一地优于最先进的方法，最高可达50％。

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