论文
论文题目: Development of Kernel-Driven Models With Fixed Hotspot Width Under a General Modeling Framework in the Thermal Infrared Domain
第一作者: Liu Xiangyang, Tang Bo-Hui, Li Zhao-Liang, Shang Guofei
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发表年度: 2021
摘  要:
英文摘要: Kernel-driven models provide an effective way for correcting the thermal radiation directionality effect. Under a general kernel-driven modeling framework proposed by Cao et al., by using three fixed-width hotspot kernels, and considering whether to combine two existing base shape kernels, this article proposed nine kernel-driven models with different coefficient requirements. Specifically, the three hotspot kernels are four-component kernel (K-GO4), three-component kernel (K-GO3), and sunlit soil kernel (K-GOg), and the two base shape kernels are temperature difference kernel (K-LSF) and emissivity kernel (K-Emi). Based on discrete anisotropic radiative transfer model, a comprehensive simulation data set, including three fraction of vegetation covers (FVC) and 36 component temperature profiles, was generated for evaluating the performances of novel models. First, the order of model performances was determined, and the result is basically independent of FVC and component temperature difference. In the case of five-parameter, four-parameter, three-parameter, and two-parameter, the best models are GO4_LSF model or GO4_Emi model, GO3_LSF model or GO3_Emi model, GOg_LSF model or GOg_Emi model, and GOg model, respectively. In addition, for three-parameter models, the comparison with two current linear kernel-driven models, i.e., LSF_Li model and Vinnikov model, was discussed in detail, and it is revealed that newly proposed GOg_LSF and GOg_Emi model in this article have a better performance.
刊物名称: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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论文类别: SCI