SPIE Journal Paper | 8 June 2024
KEYWORDS: Atmospheric modeling, Semantics, Edge detection, Image segmentation, Silver, Adhesion, Remote sensing, Lithium, Data modeling, Performance modeling
Agricultural greenhouses have a negative impact on the ecological environment while bringing huge economic and social benefits. Therefore, it is of great significance to obtain greenhouse information in a timely and accurate manner. Due to the complex spectral characteristics and dense spatial distribution characteristics of greenhouses, although the extraction of greenhouses based on a single semantic segmentation model can extract the area with high precision, the segmentation process has a serious problem of boundary adhesion between greenhouses, which makes it difficult to accurately obtain the quantity of greenhouses. To address this, our study proposes a method for greenhouse extraction that integrates semantic segmentation and edge constraints, using high-spatial-resolution remote sensing images to accurately extract the area and quantity of greenhouses. This method employs an improved semantic segmentation model (AtDy-D-LinkNet) to extract the greenhouse area, which embeds a convolutional attention module into the D-LinkNet and adopts a dynamic upsampling strategy, achieving precise greenhouse extraction. Experiments demonstrate that the improved model increased the recall, precision, F1 score, and intersection over union by 1.68%, 2.27%, 1.93%, and 3.54%, respectively, compared to the original model. To address the significant edge adhesion issue in semantic segmentation and accurately extract the quantity of greenhouses, we developed an edge constraint approach. This approach uses an edge detection model to extract greenhouse boundaries, further constrains the greenhouse surfaces, separates adhered greenhouses, and outputs vector patches representing individual greenhouses, thereby achieving precise greenhouse quantity extraction. The experiments show that this method effectively combines the advantages of semantic segmentation and edge detection. It not only ensures the accuracy of greenhouse area extraction but also effectively solves the boundary adhesion issue, significantly improving quantity extraction accuracy, resulting in vector patches that align with the actual area, quantity, and spatial distribution of greenhouses. This can provide a data foundation for greenhouse management and planning in agriculture.
