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通过深度自动编码器网络实现广义线性混合非线性波动模型的高光谱分离
算法
2026/09/21
摘要：光谱解混是高光谱图像处理中一项重要的任务，用于将观测到的各种物质的混合光谱数据分离成单个像素。近年 来，非线性光谱解混受到了特别的关注，因为线性混合在许多条件下并不合适。然而，现有的非线性解混方法通常基于 对固有非线性的特定假设，当应用于偏离原始假设的条件时，它们可能无效。因此，这些方法不适用于具有未知非线性 特性的场景。本文提出了一种基于深度自动编码网络的无监督非线性光谱解混方法，该方法适用于广义线性混合/非线性 波动模型，该模型由线性混合分量和依赖于端元和丰度的加性非线性混合分量组成。所提出的方法受益于深度神经网络 的通用建模能力，可以通过自动编码网络从数据本身学习非线性混合分量的固有非线性，而不是依赖于假设的形式。使 用数值合成、标记的实验室创建数据和真实机载数据进行的大量实验表明，与最先进的方法相比，该方法具有通用性和 有效性。
关键词：高光谱成像、非线性光谱解混、深度学习、自动编码器网络。
期刊：Transactions on Geoscience and Remote Sensing- IEEE
设备：GaiaField
Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Model
Abstract：Spectral unmixing is an important task in hyperspectral image processing for separating the mixed spectral data pertaining to various materials observed individual pixels. Recently, nonlinear spectral unmixing has received particular attention because a linear mixture is not appropriate under many conditions. However, existing nonlinear unmixing approaches are often based on specific assumptions regarding the inherent nonlinearity, and they can be ineffective when applied to conditions deviating from the original assumptions. Therefore, these approaches are not well suited to scenes with unknown nonlinearity characteristics. This paper presents an unsupervised nonlinear spectral unmixing method based on a deep autoencoder network that applies to a generalized linear-mixture/nonlinear fluctuation model, consisting of a linear mixture component and an additive nonlinear mixture component that depends on both endmembers and abundances. The proposed approach benefits from the universal modeling ability of deep neural networks to learn the inherent nonlinearity of the nonlinear mixture component from the data itself via the autoencoder network, rather than relying on an assumed form. Extensive experiments with numerically synthetic, labeled laboratory-created data and real airborne data, illustrate the generality and effectiveness of this approach compared with state-of- the-art methods.
Keywords：Hyperspectral imaging, nonlinear spectral unmixing, deep learning, autoencoder network.
