Download or read book Non-linear Unmixing for Hyperspectral Reflectance of Pigment Mixtures Using Derivative Transformation and Convolutional Neural Networks written by Sohyun An and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cultural heritage science encompasses the conservation, analysis, and interpretation of artworks, objects, and materials that hold archaeological, historic, and artistic value. It involves a wide range of disciplines and methodologies aimed at preserving and understanding our collective cultural heritage. Hyperspectral imaging (HSI) is recognized as a highly effective tool in the field of Cultural Heritage Science. Its primary advantage stems from its capability to acquire reflectance data from across a wide range of spectral bands for each pixel, providing high-dimensional vectors that enable advanced visual data analysis. Its superior spectral resolution makes it particularly effective for polychrome artifact characterization, facilitating non-invasive investigations of original painting materials, including pigments and binders. Moreover, HSI enables the identification of alteration products and underdrawings providing valuable insights into the composition and physical history of cultural artifacts. However, owing to the intricate hierarchical structure of painted artifacts, nonlinear spectral unmixing methods are often used to process HSI data. These methods facilitate the breakdown of pigment mixtures and enable the detection of individual components. In recent years, there has been a growing emphasis on data-driven approaches to effectively manage and analyze the vast volumes of hyperspectral imaging data involved in advanced spectral unmixing techniques. In line with this trend, this research endeavors to harness the power of convolutional neural networks (CNNs) for the nonlinear unmixing of hyperspectral reflectance spectra in pigment mixtures. By leveraging the capabilities of CNNs, this study aims to enhance the efficiency and accuracy of spectral unmixing, paving the way for a more robust and comprehensive analysis of complex pigment mixtures in cultural heritage objects. In contrast to traditional approaches that heavily rely on predefined assumptions, by harnessing the power of machine learning and leveraging the inherent patterns within the data, this approach enables the extraction of meaningful and significant results without being constrained by predetermined assumptions. In this research, the neural network's training set was limited to a small number of samples with various fractions of indigo and yellow ochre. Exploiting the extensive spectral information provided by each pixel in hyperspectral imaging (HSI), a substantial dataset for training was generated. Through rigorous experimentation involving different combinations of input features, it was determined that the optimal input configuration consists of the reflectance data derived from the HSI, complemented by the inclusion of the first derivative transformation value. The developed multi-input convolutional neural network model demonstrates high accuracy in estimating the proportion of indigo and yellow ochre in the mixture, as evidenced by the mean absolute error, mean squared error, and variance score of 0.01, 0.03, and 0.9999, respectively. Moreover, the model's predicted average values closely align with the correct fractions, further affirming its precision. Notably, even for fractions that were not included in the training set, the model demonstrates a high level of accuracy, albeit slightly lower than the results for the trained set. In conclusion, this research establishes the efficacy of the multi-input CNN model in accurately estimating the fraction of indigo and yellow ochre in pigment mixtures. The model successfully leverages hyperspectral imaging (HSI) data to index each pixel and provide precise mapping of the pigments. Moreover, the model's versatility enables its application to different pigment mixtures with minimal additional effort, encompassing the fabrication of mixtures, HSI data acquisition, and CNN training.