融合光谱指数与高分影像的塑料大棚语义分割模型

A Semantic Segmentation Model for Mapping Plastic Greenhouse Based on Spectral Index and High-Resolution Imagery

准确获取塑料大棚的空间分布及其动态变化信息对农业发展规划、粮食评估、生态环境监测具有重要意义。高分辨率遥感影像可提供精细的塑料大棚形状、边界等细节信息,符合精准农业的发展需求,在重点区域塑料大棚精准调查中具有不可替代的优势。然而,目前基于高分辨率影像的塑料大棚解译模型面临先验信息不足、难以兼顾解译精度和模型复杂度的难题。针对此问题,提出一种融合新型塑料大棚指数(advanced plastic greenhouse index, APGI)与高分红绿蓝(red green blue,RGB)影像的塑料大棚语义分割模型。该模型主要由APGI语义分支、RGB语义分支和RGB细节分支等组成,并使用注意力机制,融合APGI指数包含的可靠先验信息和高分RGB影像提供的空间细节信息。此外,提出一种边界信息引导的模型训练机制,提升模型对塑料大棚边界像素的精确识别能力。研究发现,在注意力机制引导的融合框架下,中分辨率的APGI指数可有效提升高分影像的塑料大棚识别能力。实验证明,所提模型在明显提升塑料大棚解译精度的基础上,大幅精简了语义分割模型的参数量和计算复杂度,满足高分辨率影像塑料大棚快速、精准解译需求。

Objectives It is of great importance to precisely acquire the spatial distribution and dynamic variations of plastic greenhouses for agricultural development planning,grain assessment,and ecological environment monitoring.High-resolution remote sensing images offer detailed information of plastic greenhouse shapes and boundaries,satisfies the requirements of precision agriculture,and have irreplaceable advantages in conducting precise surveys of plastic greenhouses in crucial regions.Nevertheless,the mapping model of plastic greenhouses meets the challenges including inadequate prior information and unbalance between mapping accuracy and model complexity.Methods This paper introduces a semantic segmentation model for plastic greenhouse that integrates the advanced plastic greenhouse index(APGI)and high-resolution red green blue(RGB)images.The proposed model primarily comprises the APGI semantic branch,RGB semantic branch,and RGB detail branch,and it incorporates an attention mechanism to effectively merge reliable prior information from the APGI index and spatial detail information derived from high-resolution RGB images.Furthermore,a model training mechanism guided by boundary information is proposed to enhance the precise recognition capability of the proposed model for plastic greenhouse boundary pixels.Results The study reveals that,within an attention-guided fusion framework,the medium-resolution APGI can significantly enhance the identification capabilities of plastic greenhouses in high-resolution images.The proposed model can not only improve the mapping accuracy of plastic greenhouses but also greatly simplifie the parameter size and computational complexity of the semantic segmentation model.Conclusions This study meets the demands for rapid and accurate extraction of plastic greenhouse in high-resolution remote sensing images.