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首页> 外文会议>Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII pt.1 >A classification approach and comparison to other object identification algorithms for hyperspectral imagery
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A classification approach and comparison to other object identification algorithms for hyperspectral imagery

机译：高光谱影像的分类方法及与其他目标识别算法的比较

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This study adapts a variety of techniques derived from multi-spectral image classification to find objects amid cluttered backgrounds in hyperspectral imagery. This study quantitatively compares the algorithms against a standard object search, the matched filter (MF) and recently developed object detector, Adaptive Cosine Estimator (ACE). These object searches require calculating the Mahalanobis distance between the average object spectral signature and the test pixel spectrum and needs the computation of a covariance matrix. The covariance matrix is generated using the entire image (Whitened Euclidean Distance, WED) or using pixels associated with the object (Maximum Likelihood Classifier, MLC). The latter computation requires a relatively large number of pixels to generate a non-singular, accurate covariance matrix. Regularizing object pixels via optimally mixing (likelihood maximization) diagonal, object, and entire image covariance matrices to generate the object covariance matrix estimate. This approximation is called the Regularized Maximum Likelihood Classifier (RMLC). The object searches MF, ACE, WED, MLC, and RMLC were applied to visibleear IR data collected from forest and desert environments. This study searched for objects using object signatures and covariance matrices taken directly from the scene and from statistically transformed object signatures and covariance matrices from another time. This study found a substantial reduction in the number of false alarms (factor of 10 to 1000) using WED, ACE, RMLC relative to MF searches for the two independent data collects. The regularization of in-scene and transformed covariance matrices substantially reduced false alarms relative to using unprocessed covariance matrices . This study adds simple, high performing algorithms to the object search arsenal.
机译：这项研究采用了从多光谱图像分类中衍生出来的各种技术，以在高光谱图像中杂乱的背景中寻找物体。这项研究定量地将算法与标准对象搜索，匹配滤波器（MF）和最近开发的对象检测器自适应余弦估计器（ACE）进行了比较。这些对象搜索需要计算平均对象光谱特征和测试像素光谱之间的马氏距离，并需要计算协方差矩阵。使用整个图像（欧氏白化距离，WED）或使用与对象关联的像素（最大似然分类器，MLC）生成协方差矩阵。后一种计算需要相对大量的像素以生成非奇异的，准确的协方差矩阵。通过对角线，对象和整个图像协方差矩阵进行最佳混合（似然最大化）来对对象像素进行正则化，以生成对象协方差矩阵估计。这种近似称为正则化最大似然分类器（RMLC）。对象搜索MF，ACE，WED，MLC和RMLC用于从森林和沙漠环境收集的可见/近红外数据。这项研究使用直接从场景中获取的对象签名和协方差矩阵以及从另一时间开始统计转换后的对象签名和协方差矩阵来搜索对象。这项研究发现，相对于MF搜索两个独立的数据收集而言，使用WED，ACE，RMLC可以大大减少错误警报的数量（从10到1000）。相对于使用未处理的协方差矩阵，场景内和变换后的协方差矩阵的正则化大大减少了误报。这项研究向目标搜索库中添加了简单，高性能的算法。

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