Spectral Semantic Analytics of Local Spaces with Neural Network Models

Abstract

This paper explores the development and application of spectral-semantic analytics methods for studying local spaces using neural network models. The approach is based on the integration of spectral data analysis (including that obtained using spectrometers and thermal imagers) with semantic models that enable the interpretation of spectral characteristics as carriers of semantic structures. The study examines methods for transforming spectral modality into linguistic and semantic modalities: this makes it possible to describe the physical properties of local spaces not only quantitatively (through spectral parameters) but also qualitatively—in the form of semantic profiles and semantic patterns. Particular attention is paid to the construction of spectral-semantic dictionaries and corresponding neural network architectures capable of identifying and formalizing the relationships between the spectral signatures of objects and their semantic load in a given context. Various types of spectrograms and spectral representations (including multi-band and hyperspectral data) are used to analyze local spaces, as well as a neural network metamodel that enables the generation of specialized spectral-semantic models for specific subject areas. Attention mechanisms are integrated into the architecture of the models, ensuring the selection of the most informative spectral ranges and spatial zones that are significant for the interpretation of meanings.