Abstract

Methods were explored for the classification of heterogeneous powder mixtures using Fourier transform infrared (FT-IR) hyperspectral image data. The images collected were non-congruent, meaning that samples of the same mixture do not have the same spatial arrangements of their components in their images. In order to classify such images on a one-image-per-object basis, dimension reduction was carried out so as to produce a score or feature vector for each image that preserved information about the heterogeneity of the sample. These feature vectors were then classified using discriminant analysis (DA) or soft independent modeling of class analogy (SIMCA). The most successful approach was the use of a median-interquartile range "super-spectrum" as the feature vector representing each image; using principal component analysis (PCA) DA classification, 87.5% of training samples were correctly classified using leave-one-out cross-validation, and 100% of a test set were correctly classified. This compares with 52.5% and 72%, respectively, when single-point spectra were used to classify the samples.

Automated tea quality classification by hyperspectral imaging

Jiewen Zhao, Quansheng Chen, Jianrong Cai, and Qin Ouyang
Appl. Opt. 48(19) 3557-3564 (2009)

Near-infrared hyperspectral circular polarization imaging and object classification with machine learning

Masato Suzuki, Kimitaka Doi, Moritsugu Sakamoto, Kohei Noda, Tomoyuki Sasaki, Nobuhiro Kawatsuki, and Hiroshi Ono
Opt. Lett. 49(3) 706-709 (2024)

Feature extraction using Mel frequency cepstral coefficients for hyperspectral image classification

Delian Liu, Xiaorui Wang, Jianqi Zhang, and Xi Huang
Appl. Opt. 49(14) 2670-2675 (2010)

Feature weighting algorithms for classification of hyperspectral images using a support vector machine

Bin Qi, Chunhui Zhao, and Guisheng Yin
Appl. Opt. 53(13) 2839-2846 (2014)

Copper concentrate dual-band joint classification using reflectance hyperspectral images in the VIS-NIR and SWIR bands

Franco Rivas, Francisco Pérez, Claudio Sandoval, Ignacio Sanhueza, Benjamín Sepúlveda, Jorge Yañez, and Sergio Torres
Appl. Opt. 62(12) 2970-2977 (2023)