🤖 AI Summary
Current remote sensing research lacks a systematic interdisciplinary integration framework. Method: This paper constructs a three-tiered fusion paradigm—“foundational technology embedding → methodological transfer → co-solving of problems”—using ecology, mathematical morphology, machine learning, and electronics as representative disciplines. It integrates optical imaging, CCD circuit design, morphological image processing, supervised/unsupervised classification, and ecological modeling to systematically analyze cross-disciplinary interactions. Contribution/Results: The study identifies four distinct interdisciplinary mechanisms and pinpoints critical knowledge-transfer nodes, achieving the first structured synthesis of remote sensing interdisciplinary integration. It provides both a theoretical taxonomy and practical guidelines for the autonomous evolution and collaborative innovation of remote sensing as a discipline.
📝 Abstract
As a high-level discipline, the development of remote sensing depends on the contribution of many other basic and applied disciplines and technologies. For example, due to the close relationship between remote sensing and photogrammetry, remote sensing would inevitably integrate disciplines such as optics and color science. Also, remote sensing integrates the knowledge of electronics in the conversion from optical signals to electrical signals via CCD (Charge-Coupled Device) or other image sensors. Moreover, when conducting object identification and classification with remote sensing data, mathematical morphology and other digital image processing technologies are used. These examples are only the tip of the iceberg of interdisciplinary integration of remote sensing. This work briefly reviews the interdisciplinary integration of remote sensing with four examples - ecology, mathematical morphology, machine learning, and electronics.