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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >FPGA Implementation of the N-FINDR Algorithm for Remotely Sensed Hyperspectral Image Analysis
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FPGA Implementation of the N-FINDR Algorithm for Remotely Sensed Hyperspectral Image Analysis

机译：用于遥感高光谱图像分析的N-FINDR算法的FPGA实现

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Hyperspectral remote sensing attempts to identify features in the surface of the Earth using sensors that generally provide large amounts of data. The data are usually collected by a satellite or an airborne instrument and sent to a ground station that processes it. The main bottleneck of this approach is the (often reduced) bandwidth connection between the satellite and the station, which drastically limits the information that can be sent and processed in real time. A possible way to overcome this problem is to include onboard computing resources able to preprocess the data, reducing its size by orders of magnitude. Reconfigurable field-programmable gate arrays (FPGAs) are a promising platform that allows hardware/software codesign and the potential to provide powerful onboard computing capability and flexibility at the same time. Since FPGAs can implement custom hardware solutions, they can reach very high performance levels. Moreover, using run-time reconfiguration, the functionality of the FPGA can be updated at run time as many times as needed to perform different computations. Hence, the FPGA can be reused for several applications reducing the number of computing resources needed. One of the most popular and widely used techniques for analyzing hyperspectral data is linear spectral unmixing, which relies on the identification of pure spectral signatures via a so-called endmember extraction algorithm. In this paper, we present the first FPGA design for N-FINDR, a widely used endmember extraction algorithm in the literature. Our system includes a direct memory access module and implements a prefetching technique to hide the latency of the input/output communications. The proposed method has been implemented on a Virtex-4 XC4VFX60 FPGA (a model that is similar to radiation-hardened FPGAs certified for space operation) and tested using real hyperspectral data collected by NASA's Earth Observing-1 Hyperion (a satellite instrument) and the Airborne Visible Infra--ned Imaging Spectrometer over the Cuprite mining district in Nevada and the Jasper Ridge Biological Preserve in California. Experimental results demonstrate that our hardware version of the N-FINDR algorithm can significantly outperform an equivalent software version and is able to provide accurate results in near real time, which makes our reconfigurable system appealing for onboard hyperspectral data processing.
机译：高光谱遥感尝试使用通常提供大量数据的传感器来识别地球表面的特征。数据通常由卫星或机载仪器收集，然后发送到处理该数据的地面站。这种方法的主要瓶颈是卫星与台站之间的（通常是减少的）带宽连接，这极大地限制了可以实时发送和处理的信息。解决此问题的一种可能方法是包括能够预处理数据的机载计算资源，从而将数据大小减小几个数量级。可重配置的现场可编程门阵列（FPGA）是一个有前途的平台，它允许硬件/软件代码签名，并有潜力同时提供强大的板载计算能力和灵活性。由于FPGA可以实现定制的硬件解决方案，因此它们可以达到很高的性能水平。此外，使用运行时重新配置，可以在运行时将FPGA的功能更新为执行不同计算所需的次数。因此，FPGA可以重新用于多种应用程序，从而减少了所需的计算资源数量。用于分析高光谱数据的最流行和广泛使用的技术之一是线性光谱解混，它依赖于通过所谓的端成员提取算法来识别纯光谱特征。在本文中，我们介绍了N-FINDR的第一个FPGA设计，这是文献中广泛使用的端成员提取算法。我们的系统包括直接内存访问模块，并实现了预取技术以隐藏输入/输出通信的延迟。拟议的方法已在Virtex-4 XC4VFX60 FPGA（类似于经过空间操作认证的辐射硬化FPGA的模型）上实施，并使用了NASA的Earth Observing-1 Hyperion（卫星仪器）和卫星所收集的真实高光谱数据进行了测试。内华达州Cuprite矿区和加利福尼亚州贾斯珀里奇生物保护区上空的机载红外成像光谱仪。实验结果表明，我们的N-FINDR算法的硬件版本可以大大优于同等软件版本，并且能够近乎实时地提供准确的结果，这使我们的可重新配置系统吸引了机载高光谱数据处理。

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