Han Sanghui, Kerekes John, Higbee Shawn, Siegel Lawrence, Pertica Alex
Appl Opt. 2019 Apr 10;58(11):2981-2993. doi: 10.1364/AO.58.002981.
While offering powerful capabilities, the high dimensionality of hyperspectral images can make information extraction a challenge. For that reason, dimension reduction is a common data processing step. For the purpose of subpixel target detection, band selection is a dimension reduction method that can optimize results as well as reduce computation costs. However, existing band selection methods that are used for subpixel target detection require background spectral reflectance signatures to compare with the target signatures. These methods work well and offer a distinct advantage over other dimension reduction methods such as principal component analysis or nonnegative matrix factorization, but only when the background information is available. In this study, we developed a method that selected bands using only the target spectral reflectance signature. We tested this method using a utility prediction model, validated the results with real images, then cross-validated the results with simulated images that were associated with perfect truth data. We studied the detection statistics for a range of bands selected using this method and compared it to the results obtained from three other band selection methods. The motivation for developing this method was to be able to reduce the number of bands prior to collection when background information was not available. For an adaptive spectral imaging system with a tunable sensor, we would be able to optimize detection for a specific target and save data handling costs associated with transmitting, storing, and disseminating the data for information extraction. This method was also simple enough to be computed using a small on-board CPU, and modify the bands' selection criteria as the target changed.
尽管高光谱图像具有强大的功能，但其高维度会使信息提取成为一项挑战。因此，降维是常见的数据处理步骤。出于亚像素目标检测的目的，波段选择是一种降维方法，它既能优化结果，又能降低计算成本。然而，现有的用于亚像素目标检测的波段选择方法需要背景光谱反射特征与目标特征进行比较。这些方法效果良好，与主成分分析或非负矩阵分解等其他降维方法相比具有明显优势，但仅在背景信息可用时才有效。在本研究中，我们开发了一种仅使用目标光谱反射特征来选择波段的方法。我们使用效用预测模型测试了该方法，用真实图像验证了结果，然后用与完美真值数据相关联的模拟图像对结果进行交叉验证。我们研究了使用该方法选择的一系列波段的检测统计数据，并将其与从其他三种波段选择方法获得的结果进行比较。开发此方法的动机是在背景信息不可用时，能够在采集前减少波段数量。对于具有可调传感器的自适应光谱成像系统，我们能够针对特定目标优化检测，并节省与传输、存储和传播用于信息提取的数据相关的数据处理成本。此方法也足够简单，可以使用小型机载CPU进行计算，并随着目标的变化修改波段选择标准。
