Abstract:

Artificial intelligence theories and technologies, particularly deep learning, demonstrate immense potential in bridging macroscopic and microscopic cognition, opening innovative pathways for cross-scale isomorphic mapping and high-fidelity information processing in traditional remote sensing paradigms, thereby propelling the profound transformation from classical physical remote sensing to quantum remote sensing paradigms. This paper proposes a new paradigm theory for remote sensing parameter inversion based on deep learning, with its core being to regard the multi-layer neuron structure of deep learning as "quantum integral transmission units" in the microscopic world, thereby organically coupling the physical radiative transfer process with high-dimensional statistical measures, achieving seamless bridging between quantum-level fluctuations and macroscopic observations. Logical analysis shows that this paradigm is based on the exponential refinement of information granularity in silicon-based computing, breaking the constraints of traditional manual coordinate system design, realizing the automatic generation of universal coordinate systems, and gradually approaching the intrinsic granularity level of nature to minimize energy information loss. Furthermore, deep learning achieves a continuous connection from quantum interactions to macroscopic understanding, with its information processing resolution gradually approaching the intrinsic level of nature as computing power increases, and significantly reducing distortion in cross-scale mapping. Computing power and energy will become core production factors, while deep learning, as a "universal coordinate system generator," experienced qualitative acceleration from the late 20th century to the early 21st century. This paradigm theory marks a milestone breakthrough in the "coordinate system construction movement" that has lasted nearly three thousand years in human cognitive history and mathematical history, formally ushering in the quantum remote sensing era. Looking to the future, the collaborative evolution of software and hardware will prompt deep learning to nurture a multi-granularity new "quantum" language, becoming a new sensory organ and new brain for human collective cognition, producing profound impacts on remote sensing technology innovation and fields such as agricultural meteorological monitoring.

Key words: artificial intelligence, AI cognitive theory, quantum remote sensing