Fast real-time onboard processing of hyperspectral imagery for detection and classification
Fast real-time onboard processing of hyperspectral imagery for detection and classification
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DOI：
10.1007/s11554-008-0106-9
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发表时间：
2009-08-01
影响因子：
3
通讯作者：
Nekovei, Reza
中科院分区：
文献类型：
--
作者：
Du, Qian;Nekovei, Reza
遥感高光谱图像具有许多重要的应用，因为其高光谱分辨率可以实现更准确的物体检测和分类。为了支持在危急情况下立即做出决策，非常需要实时船上实施。本文研究了针对不同格式的图像数据的几种流行检测和分类算法的实时实现。提出了一种通过使用一小部分像素来评估数据统计来加速实时实现的有效方法。研究了要使用的适当像素百分比的经验规则，这会降低计算复杂性并简化硬件实现。还提供了总体系统架构。
Remotely sensed hyperspectral imagery has many important applications since its high-spectral resolution enables more accurate object detection and classification. To support immediate decision-making in critical circumstances, real-time onboard implementation is greatly desired. This paper investigates real-time implementation of several popular detection and classification algorithms for image data with different formats. An effective approach to speeding up real-time implementation is proposed by using a small portion of pixels in the evaluation of data statistics. An empirical rule of an appropriate percentage of pixels to be used is investigated, which results in reduced computational complexity and simplified hardware implementation. An overall system architecture is also provided.