METHODOLOGY FOR CREATING INTELLIGENT INFORMATION TECHNOLOGIES FOR RECOGNITION AND PROCESSING OF MULTISPECTRAL AEROSPACE IMAGES
DOI:
https://doi.org/10.34185/1562-9945-5-166-2026-23Keywords:
multispectral aerospace images, intelligent information technology, methodology for creation, heterogeneous data, synergistic processing, multilevel integration, spatial-spectral-semantic representation, multiagent analysis, multimodal interpretation, decision supportAbstract
Heterogeneous Earth-observation data and rapidly expanding machine-learning, multiagent, and multimodal models require a methodology that creates coherent intelligent information technologies rather than isolated processing pipelines. The purpose of the study is to develop and formalize a methodology for creating intelligent information technologies for recognition and processing of multispectral aerospace images through synergistic processing of heterogeneous data. The methodology combines systems analysis, set-theoretic mappings, graph models, multilevel integration, multi-criteria configuration selection, and structural-functional verification. A scene is represented as a multidimensional information object with spatial, spectral, textural, contextual, temporal, and semantic characteristics. The principal scientific result is a methodology that integrates a system of creation principles, the Ψ–Φ–Λ cycle of data harmonization, representation formation, and evidence-aware decision integration, adaptive task-dependent configuration, multiagent cooperation, multimodal interpretation, and end-to-end control of quality, uncertainty, and provenance. The creation procedure includes problem formalization, admissible source and component selection, data harmonization, integrated representation, intelligent recognition, evidence reconciliation, explained decision formation, and feedback-driven reconfiguration. Structural-functional verification establishes mechanisms for completeness, consistency, modularity, extensibility, adaptability, traceability, and interpretability. The methodology provides a design basis for monitoring and decision-support systems for natural and anthropogenic objects.
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