The similarity in the hue of corroded surfaces and coated surfaces, dust, vegetation, etc. leads to visual ambiguity which is challenging to eliminate using existing image classification/segmentation techniques. Furthermore, existing methods lack the ability to identify the source of corrosion, which plays a vital role in framing the corrosion mitigation strategies. The goal of this study to employ hyperspectral imaging (1) to detect corroded surfaces under visually ambiguous scenarios and (2) identify the source of corrosion in such scenarios. To this end, three different corrosive media, namely, (1) 1M hydrochloric acid (HCl), 2) 3.5 wt.% sodium chloride solution (NaCl), and (3) 3 wt.% sodium sulfate solution (Na₂SO₄), are employed to generate chemically distinctive corroded surfaces. The hyperspectral imaging sensor is employed to obtain the visible and near infrared (VNIR) spectra (397 nm–1004 nm) reflected by the corroded/coated surfaces. The intensity of the reflectance in various spectral bands are considered as the descriptive features in this study, and the training and test datasets were generated consisting of 35,000 and 15,000 data points, respectively. SVM classifier is trained and then its efficacy on the test data is assessed. Furthermore, validation datasets are employed and the generalization ability of the trained SVM classifier is verified. The results from this study revealed that the SVM classifier achieved an overall accuracy of 94% with the misclassifications of 18% and 13% in the case of NaCl and Na₂SO₄ corrosion, respectively. Reflectance spectra obtained in the VNIR region was found to eliminate the visual ambiguity between the corroded and coated surfaces and, identify the source of corrosion accurately. Further, the range of key wavelengths of the spectra that play an important role in the distinguishability of coating and chemically distinctive corroded surface were identified to be 500–520 nm, 660–680 nm, 760–770 nm, and 830–850 nm.

腐蚀表面和涂层表面、灰尘、植被等色调的相似性导致视觉模糊，使用现有的图像分类/分割技术很难消除这种模糊。此外，现有方法缺乏识别腐蚀源的能力，这在制定腐蚀缓解策略中起着至关重要的作用。本研究的目标是采用高光谱成像 (1) 在视觉模糊的情况下检测腐蚀表面和 (2) 在这种情况下识别腐蚀源。为此，三种不同的腐蚀介质，即（1）1M 盐酸（HCl），2）3.5 wt.% 氯化钠溶液（NaCl），和（3）3 wt.% 硫酸钠溶液（Na ₂ SO ₄)，用于生成具有化学特征的腐蚀表面。高光谱成像传感器用于获得由腐蚀/涂层表面反射的可见光和近红外（VNIR）光谱（397 nm-1004 nm）。本研究将各种光谱波段的反射强度视为描述性特征，生成的训练和测试数据集分别由 35,000 和 15,000 个数据点组成。训练 SVM 分类器，然后评估其对测试数据的有效性。此外，使用验证数据集，并验证了经过训练的 SVM 分类器的泛化能力。这项研究的结果表明，在 NaCl 和 Na ₂的情况下，SVM 分类器的总体准确率达到了 94%，错误分类率分别为 18% 和 13%。SO ₄分别腐蚀。发现在 VNIR 区域获得的反射光谱可以消除腐蚀和涂层表面之间的视觉模糊，并准确识别腐蚀源。此外，在涂层和化学特征腐蚀表面的可区分性中起重要作用的光谱关键波长范围被确定为 500-520 nm、660-680 nm、760-770 nm 和 830-850 nm。