无损检测是高光谱遥感应用研究热点之一。 苹果在采摘、 运输过程中易发生轻微机械损伤而影响其品质。 使用高光谱成像系统分别采集54个轻微损伤的“黄香蕉”与“烟台富士”苹果可见-近红外波段(400~1 000 nm)的图像, 提取苹果损伤区域的均值波谱曲线, 对其进行最小噪声分离变换和基于几何顶点端元原理提取端元波谱, 计算损伤区域波谱和端元波谱的光谱角, 构建了端元提取光谱角苹果轻微机械损伤检测模型。 通过设定光谱角阈值分别检测“黄香蕉”与“烟台富士”苹果轻微机械损伤, 并与MNF变换、 PCA方法检测精度进行对比分析, 结果表明EESA模型检测苹果轻微机械损伤的精度最高, 检测正确率分别达到94.44%和90.07%。

Nondestructive detection is one of the hottest spots in the application of hyperspectral remote sensing. The apple is easy to produce slight mechanical injuries that affects its quality in the process of picking and transporting. The hyperspectral images of 54 “yellow banana” and “Yantai Fushi” apples with slight injuries in the visible and near-infrared (400~1 000 nm) ranges are acquired; the mean spectral curves of injury regions on apples are extracted; the endmember spectrum are extracted based on minimum noise fraction (MNF) and geometric vertex principle; and the spectral angle is calculated between spectral of injury region and endmember spectral; a model of endmember extraction spectral angle (EESA) is constructed to detect slight mechanical injuries on apples. The slight mechanical injuries on “yellow banana” and “Yantai Fushi” apples are detected by setting spectral angle threshold, and the detection accuracy is compared with MNF and principal component analysis (PCA) method. The results show that the accuracy of EESA model is the highest, and the detection accuracy rate reaches 94.44% and 90.07% respectively.