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Target detection in multispectral images using the spectral co-occurrence matrix and entropy thresholding

机译：使用光谱共生矩阵和熵阈值的多光谱图像目标检测

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Relative entropy thresholding techniques have been used for segmentation of objects from background in gray-level images. These techniques are related to entropy-based segmentations computed for the statistics of a spatial co-occurrence matrix. For detection of spectrally active targets such as chemical vapor clouds in multispectral or hyper-spectral imagery, a spectral co-occurrence matrix is employed. Using the entropy of various regions of the matrix, thresholds can be derived that will segment an image family based on the spectral characteristics of the intended target. Experiments are presented that show the detection of a chemical vapor cloud in multispectral thermal imagery. Several manners of dividing the co-occurrence matrix into regions are explored. Thresholds are determined on both a local and global basis and compared. Locally generated thresholds are treated as a distribution and separated into classes. The point of class separation is used as a global threshold with improved results.
机译：相对熵阈值技术已用于从灰度图像中的背景中分割对象。这些技术与为空间共现矩阵的统计而计算的基于熵的分割有关。为了检测光谱活跃目标（例如多光谱或高光谱图像中的化学蒸气云），使用光谱共现矩阵。使用矩阵的各个区域的熵，可以导出阈值，该阈值将基于预期目标的光谱特征对图像族进行分段。实验表明，在多光谱热成像中可以检测到化学蒸气云。探索了将共现矩阵划分为区域的几种方式。在本地和全球范围内确定阈值并进行比较。本地生成的阈值被视为分布，并分为几类。类分离点被用作全局阈值，从而改善了结果。

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