Anomaly detection from remote sensing images is to detect pixels whose spectral signatures are different from their background. Anomalies are often man-made targets. With such target signatures being unknown, anomaly detection has many important applications, such as water quality monitoring, crop stress surveying, and law enforcement-related uses, where prior information of targets is often unavailable. The key to success is accurate background modeling. Anomaly detection from remote sensing images is challenging because spatial coverage is very large and the background is highly heterogeneous. For pixel-based anomaly detection, computing cost in background modeling and a spatial-convolution-type detection process is very expensive. Thus, parallel and distributed computing is critical in reducing execution time, which can fit the need for real-time or near real-time detection from airborne and spaceborne platforms in support of immediate decision-making. This article reviews the recent advances in anomaly detection from hyperspectral remote sensing images and their implementation using parallel and distributed systems. The classical methods, i.e., the Reed–Xiaoli (RX) algorithm and its variants, including its real-time processing version, are illustrated in commodity graphic processing units (GPUs), cloud, and field-programmable gate array (FPGA) implementations. Practical issues and future development trends are also discussed.

中文翻译：

---

用于高光谱遥感图像异常检测的并行分布式计算


遥感图像的异常检测是检测光谱特征与其背景不同的像素。异常通常是人为目标。由于此类目标特征未知，异常检测具有许多重要的应用，例如水质监测、作物胁迫调查和执法相关用途，而这些用途通常无法获得目标的先验信息。成功的关键是准确的背景建模。遥感图像的异常检测具有挑战性，因为空间覆盖范围非常大并且背景高度异质。对于基于像素的异常检测，背景建模和空间卷积型检测过程的计算成本非常昂贵。因此，并行和分布式计算对于减少执行时间至关重要，这可以满足机载和星载平台实时或近实时检测的需求，以支持即时决策。本文回顾了高光谱遥感图像异常检测的最新进展及其使用并行和分布式系统的实现。经典方法，即 Reed-Xiaooli (RX) 算法及其变体，包括其实时处理版本，在商品图形处理单元 (GPU)、云和现场可编程门阵列 (FPGA) 实现中进行了说明。还讨论了实际问题和未来发展趋势。