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.
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