首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Object detection by using 'whitening/dewhitening' to transform target signatures in multitemporal hyperspectral and multispectral imagery
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Object detection by using 'whitening/dewhitening' to transform target signatures in multitemporal hyperspectral and multispectral imagery

机译:通过“变白/变白”在多时间高光谱和多光谱图像中变换目标特征的目标检测

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摘要

Changes in atmosphere, ground conditions, scene temperature, solar illumination, and sensor response can significantly affect the detected multispectral and hyperspectral data. Using uncorrected spectral target signatures in spectral matched filter searches therefore results in target detection with concomitant high false-alarm rates due to changes in multispectral and hyperspectral images. This letter introduces the use of the whitening/dewhitening (WD) transform to help correct target spectral signatures under varying conditions. An important feature of this transform is that it does not require subpixel registration between images collected at two distinct times. The transform was tested on images taken from two very different data collects using different sensors, targets, and backgrounds. In one dataset, the transform was applied to hyperspectral images taken from airborne longwave infrared sensor binned to 30 bands and the other data collect used images of a variety of tanks, trucks, calibration panels that were collected using bore-sighted broadband visible, shortwave infrared, midwave infrared, and longwave infrared staring array sensors. Target spectral signatures were transformed using imagery of spatially overlapping regions from datasets collected at different times and processed using the whitening and then dewhitening transform (inverse of a whitening transform). Use of the WD transform yielded a large target-to-clutter ratio (TCR) and was compared to the TCR derived from other transforms that approximated the cross-covariance matrix. In addition, the WD-transformed signatures applied in a matched filter search found targets (some concealed behind vegetative foliage or underneath camouflage) with low false-alarm rates as shown in a receiver operator characteristic curve. This letter demonstrates that the WD transform enhances searches for concealed targets in multisensor and hyperspectral data.
机译:大气,地面条件,场景温度,太阳光照和传感器响应的变化会严重影响检测到的多光谱和高光谱数据。因此,由于多光谱和高光谱图像的变化,在光谱匹配滤波器搜索中使用未校正的光谱目标特征会导致目标检测并伴随着较高的虚警率。这封信介绍了增白/去增白(WD)变换的用法,以帮助纠正在不同条件下的目标光谱特征。该变换的重要特征是它不需要在两个不同时间收集的图像之间进行子像素配准。在使用不同的传感器,目标和背景从两个截然不同的数据集获取的图像上测试了该变换。在一个数据集中,将变换应用于从装在30个波段中的机载长波红外传感器拍摄的高光谱图像,而其他数据则收集各种油箱,卡车,标定面板的使用过的图像,这些图像是使用具有孔眼的宽带可见,短波红外收集的,中波红外和长波红外凝视阵列传感器。使用来自在不同时间收集的数据集的空间重叠区域的图像来变换目标光谱特征,然后使用先变白再进行变白(反白化)处理进行处理。使用WD变换会产生较大的目标杂波比(TCR),并将其与从其他近似互协方差矩阵的变换得出的TCR进行比较。另外,在匹配的过滤器中应用的WD转换签名搜索发现目标(错误隐藏率低的目标(有些隐藏在植物的叶子后面或伪装下面),如接收方操作员特征曲线所示。这封信表明WD变换可增强对多传感器和高光谱数据中隐藏目标的搜索。

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