MODERN METHODS OF HYPERSPECTRAL IMAGE PROCESSING: SYSTEM ANALYSIS, ALGORITHMS AND PROSPECTS FOR APPLICATION IN CONSTRUCTION DIAGNOSTICS
Abstract
The relevance of this study is determined by the growing interest in hyperspectral imaging as a tool for non-destructive testing of building materials and structures, as well as by the insufficient systematization of modern methods for processing such data. The aim of the work is to provide a systematic analysis of HSI processing algorithms, identify their advantages and limitations, and determine the prospects for their application in construction diagnostics. The study examines the specific features of hyperspectral data, including high dimensionality, noise, calibration errors, atmospheric distortions, and the shortage of labeled datasets. The evolution of approaches is shown: from classical machine learning methods and manual feature engineering to deep neural networks. Dimensionality reduction methods, kNN classifiers, Bayesian models, logistic regression, Random Forest, SVM, and MLP are analyzed, along with methods for incorporating spectral-spatial context. Special attention is paid to modern deep learning architectures: 1D, 2D, and 3D CNNs, RNNs, LSTM/GRU models, hybrid CNN–RNN models, transformers, and CNN–Transformer schemes. Transfer learning, semi-supervised learning, self-supervised learning, few-shot learning, meta-learning, and domain adaptation are considered separately as ways to overcome the limited availability of labeled data. The approaches are compared in terms of data requirements, computational complexity, robustness to noise, and their ability to account for spectral and spatial dependencies. It is shown that the most promising models for construction diagnostics are hybrid models that combine local convolutional features, the global context of attention mechanisms, and the possibility of fine-tuning on small specialized datasets. The paper summarizes the current state of the field and forms a basis for selecting methods for defect detection, moisture assessment, corrosion analysis, and evaluation of degradation in building structures. The conclusions formulated in the study can be used when designing experimental protocols and selecting architectures for further applied research in the field of building monitoring
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