GPU implementation of fully constrained linear spectral unmixing for remotely sensed hyperspectral data exploitation
GPU implementation of fully constrained linear spectral unmixing for remotely sensed hyperspectral data exploitation
复制标题
用于遥感高光谱数据开发的完全约束线性光谱分解的 GPU 实现
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
Chein
中科院分区：
文献类型：
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作者：
S. Sánchez;G. Martín;A. Plaza;Chein
光谱分解是遥感高光谱数据开发的一项重要任务。在自然环境中收集的光谱特征总是在成像仪器的地面瞬时视场的空间范围内发现的各种材料的纯特征的混合。光谱分解的目的是推断场景每个像素处的纯光谱特征（称为端元）和物质分数（称为分数丰度）。光谱混合分析的标准技术是线性光谱分解，它假设光谱仪收集的光谱可以以端元的线性组合的形式表示，并根据相应的丰度加权，期望遵守两个约束，即所有丰度都应该是非负的，并且给定像素的丰度之和应该是统一的。文献中已经开发了几种用于无约束、部分约束和完全约束线性光谱解混合的技术，这些技术的计算成本可能很高（特别是对于具有大量端元的复杂高维场景）。在本文中，我们开发了无约束、部分约束和完全约束线性光谱解混合算法的新并行实现。这些实现是在可编程图形处理单元（GPU）中开发的，这是商品计算领域令人兴奋的发展，非常适合机载数据处理场景的要求，在这种场景中，必须使用低重量和低功耗的集成组件来减少任务有效负载。我们的实验是在纽约市世界贸易中心区域收集的高光谱场景中进行的，表明所提出的实现比最新一代 Tesla C1060 GPU 架构中的相应串行版本提供了相关的加速。
Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. The spectral signatures collected in natural environments are invariably a mixture of the pure signatures of the various materials found within the spatial extent of the ground instantaneous field view of the imaging instrument. Spectral unmixing aims at inferring such pure spectral signatures, called endmembers, and the material fractions, called fractional abundances, at each pixel of the scene. A standard technique for spectral mixture analysis is linear spectral unmixing, which assumes that the collected spectra at the spectrometer can be expressed in the form of a linear combination of endmembers weighted by their corresponding abundances, expected to obey two constraints, i.e. all abundances should be non-negative, and the sum of abundances for a given pixel should be unity. Several techniques have been developed in the literature for unconstrained, partially constrained and fully constrained linear spectral unmixing, which can be computationally expensive (in particular, for complex highdimensional scenes with a high number of endmembers). In this paper, we develop new parallel implementations of unconstrained, partially constrained and fully constrained linear spectral unmixing algorithms. The implementations have been developed in programmable graphics processing units (GPUs), an exciting development in the field of commodity computing that fits very well the requirements of on-board data processing scenarios, in which low-weight and low-power integrated components are mandatory to reduce mission payload. Our experiments, conducted with a hyperspectral scene collected over the World Trade Center area in New York City, indicate that the proposed implementations provide relevant speedups over the corresponding serial versions in latest-generation Tesla C1060 GPU architectures.