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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Automatic spectral target recognition in hyperspectral imagery
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Automatic spectral target recognition in hyperspectral imagery

机译：高光谱图像中的自动光谱目标识别

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摘要

Automatic target recognition (ATR) in hyperspectral imagery is a challenging problem due to recent advances of remote sensing instruments which have significantly improved sensor's spectral resolution. As a result, small and subtle targets can be uncovered and extracted from image scenes, which may not be identified by prior knowledge. In particular, when target size is smaller than pixel resolution, target recognition must be carried out at subpixel level. Under such circumstance, traditional spatial-based image processing techniques are generally not applicable and may not perform well if they are applied. The work presented here investigates this issue and develops spectral-based algorithms for automatic spectral target recognition (ASTR) in hyperspectral imagery with no required a priori knowledge, specifically, in reconnaissance and surveillance applications. The proposed ASTR consists of two stage processes, automatic target generation process (ATGP) followed by target classification process (TCP). The ATGP generates a set of targets from image data in an unsupervised manner which will subsequently be classified by the TCP. Depending upon how an initial target is selected in ATGP, two versions of the ASTR can be implemented, referred to as desired target detection and classification algorithm (DTDCA) and automatic target detection and classification algorithm (ATDCA). The former can be used to search for a specific target in unknown scenes while the latter can be used to detect anomalies in blind environments. In order to evaluate their performance, a comparative and quantitative study using real hyperspectral images is conducted for analysis.
机译：由于遥感仪器的最新进步，自动目标识别（ATR）在高光谱图像中是一个具有挑战性的问题，这是具有显着提高的传感器的光谱分辨率的遥感仪器。结果，可以从图像场景中揭开并提取小和微妙的目标，这可能无法通过先验知识来识别。特别地，当目标大小小于像素分辨率时，必须在子像素级别执行目标识别。在这种情况下，传统的基于空间的图像处理技术通常不适用，如果应用它们可能无法执行良好。这里提出的工作调查了这个问题，并在高光谱图像中开发了基于频谱的算法（Astr），没有先验的知识，特别是在侦察和监视应用中。所提出的ast由两个阶段过程组成，自动目标生成过程（ATGP），然后是目标分类过程（TCP）。 ATGP以不经过监视的方式从图像数据生成一组目标，其随后将被TCP分类。根据如何在ATGP中选择初始目标的方式，可以实现两个版本的ast，称为期望的目标检测和分类算法（DTDCA）和自动目标检测和分类算法（ATDCA）。前者可用于在未知场景中搜索特定目标，而后者可用于检测盲环境中的异常。为了评估它们的性能，进行使用真实高光谱图像的比较和定量研究进行分析。

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