J. Eur. Opt. Society-Rapid Publ. 22, 32( 2026) 325
based on the infrared radiation rendering of solid targets, which considers the spontaneous radiation and environmental radiation of ships. The infrared simulation results are compared and verified based on experimental data. The simulation calculation of focal plane radiation characteristics for ship target is realized based on the full chain of optical remote sensing detection. The ship target radiation characteristics under different sea surface conditions and different imaging time are analyzed by Z Jiang [ 6 ]. Zhang C W [ 7 ] proposed a high-frequency method by using graphics processing unit( GPU) parallel acceleration technique, simulated signals of ships at different seas. Wang M [ 8 ] proposed a data-driven infrared radiation modeling method, which optimizes the model accuracy by comparing measured data with theoretical models, and is used to simulate the infrared characteristics of marine targets under typical sea conditions. Song Bo [ 9 ] proposed a high-resolution remote sensing imaging simulation method for marine targets, focusing on the simulation method of the imaging process of the coupling effect between sea surface targets and seawater under high resolution. Jiang Le et al. [ 10, 11 ] comprehensively considered the influence of environmental factors on the infrared radiation characteristics of the target and explored the radiation modeling and simulation method of infrared scene. Meanwhile, some scholars have also explored methods for calculating the radiation characteristics of ship targets [ 12, 13 ] and simulation methods for high-confidence infrared background imaging characteristics [ 14, 15 ], providing a theoretical basis for the simulation of infrared characteristics and exploration of characteristic laws of typical targets.
The above research has made significant progress in understanding the infrared characteristics of ships, but the following shortcomings still exist. The complex port environment is affected by the diurnal cycle, variations in solar radiation, and the difference in thermal inertia between land and sea. The infrared radiation characteristics of the target and background may tend to be consistent or even reversed at certain times. This phenomenon is called“ thermal crossover.” Existing research on ship infrared imaging simulation generally ignores the“ thermal crossover” phenomenon caused by the dynamic changes in heat exchange under complex port backgrounds, making it difficult to accurately depict the temporal evolution of the infrared contrast of ship targets and background under space-based conditions. The problem essentially stems from the coupling and reversal relationship between the target and background temperatures over time, and there is an urgent need to establish a physical modeling framework that can accurately describe its time-varying characteristics. Although some scholars have used remote sensing data to model the background temperature [ 16 – 18 ] or used deep learning methods to extrapolate the spatiotemporal characteristics of background radiation [ 14, 15 ], the applicability of their results in complex scenarios is still limited due to the temporal constraints of the data and the insufficient physical interpretability of the models [ 19 – 21 ]. Therefore, if research on radiation coupling modeling of targets and backgrounds under time-series conditions is carried out, it will be of great significance for revealing the evolution law of ship infrared characteristics and improving the realism of imaging simulation.
To address the shortcomings of previous research, a spatiotemporal reconstruction method for the port background temperature, which integrates meteorological data and topographic features based on Landsat 8 remote sensing data, is proposed in this paper. First, based on environmental meteorological data, the Kriging algorithm is used to achieve a preliminary expansion of surface temperature under different temporal conditions. Then, topographic factors such as elevation, slope, and aspect are taken into account to establish a modulation model for surface radiation balance and energy exchange. Last, the background temperature is finely corrected and reconstructed with high precision. This method effectively improves the spatial continuity and physical consistency of surface temperature retrieval, providing reliable support for the simulation of ground feature background and environmental change analysis in complex terrain areas.
2 Port infrared scene modeling
From a space-based platform, the background includes not only the water surface and dock facilities, but is also subject to complex influences from atmospheric conditions, weather changes, and environmental heat sources. To reflect the radiation distribution in a real environment, a method of inverting remote sensing data to obtain the port background temperature field is used, and the infrared characteristics of different surfaces such as the sea surface, cement wharf, and building facilities are modeled to achieve highprecision characterization of multi-source background radiation. Figure 1 is a schematic diagram of the space-based optoelectronic system detecting targets.
The total scene radiation distribution received by the detector [ 22 ] can be expressed as:
L sensor ðkÞ ¼ s a L " t ðkÞþL " b ðkÞ þ La " ðkÞ ð1Þ
where s a is the atmospheric upward transmittance; L " t is the target upward radiation; L " b is the background upward radiation; and L " a is the atmospheric path upward radiation. The total background radiation includes its own radiation and reflected radiation. It is necessary to classify the background materials and establish radiative transfer models for different materials. The port background includes ocean and land, the total background radiance [ 22 ] can be expressed as:
Z k2
L b ðkÞ ¼ L " b; self ðkÞþL " b; ref ðkÞ
¼ e bðkÞ c 1 p k 1
½ expðc 2 = kT b Þ�1Š dk � þs s E sun cosh i BRDF b h i u i; h r u r þ qb L sky ð2Þ
where e b is the emissivity of different background materials; c 1 is 1.19110 8 W / lmsrm 2; c 2 is 1.438810 4 lmK; T b is the background temperature distribution; s s is the