Multi-threshold image segmentation techniques based on intelligent optimization algorithms show great potential in low-cost,real-time applications.These methods are efficient even with limited computational resources....Multi-threshold image segmentation techniques based on intelligent optimization algorithms show great potential in low-cost,real-time applications.These methods are efficient even with limited computational resources.This paper proposes a multi-strategy improved red-billed blue magpie optimizer(MIRBMO)for Kapur multi-threshold image segmentation,aiming to enhance segmentation quality.First,Sobol sequences with elite reverse learning are used to optimize the distribution of the initial population,accelerating the optimization process.Second,lens imaging reverse learning is introduced to help the algorithm escape local optima.Finally,the golden sine strategy is adopted to increase the search space diversity and explore potential optimal solutions.The algorithm’s performance is evaluated using the 8 classic benchmark test functions,and results show that MIRBMO outperforms red-billed blue magpie optimizer(RBMO)in optimization capability and demonstrates clear advantages over other intelligent optimization algorithms.When applied to Kapur multi-threshold segmentation,MIRBMO yields a threshold combination with higher entropy values and produces segmented images with superior peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),and feature similarity index measure(FSIM)values,indicating its strong application potential.展开更多
基金Supported by the National Key R&D Program of China:Science and Technology Innovation 2030-‘New Generation Artificial Intelligence’Major Project(2022ZD0119000)the Natural Science Foundation of Shaanxi Province(2025JC-YBMS-736,2025JC-YBMS-343)the Shaanxi Province Key Research and Development Project(2025CY-YBXM-061).
文摘Multi-threshold image segmentation techniques based on intelligent optimization algorithms show great potential in low-cost,real-time applications.These methods are efficient even with limited computational resources.This paper proposes a multi-strategy improved red-billed blue magpie optimizer(MIRBMO)for Kapur multi-threshold image segmentation,aiming to enhance segmentation quality.First,Sobol sequences with elite reverse learning are used to optimize the distribution of the initial population,accelerating the optimization process.Second,lens imaging reverse learning is introduced to help the algorithm escape local optima.Finally,the golden sine strategy is adopted to increase the search space diversity and explore potential optimal solutions.The algorithm’s performance is evaluated using the 8 classic benchmark test functions,and results show that MIRBMO outperforms red-billed blue magpie optimizer(RBMO)in optimization capability and demonstrates clear advantages over other intelligent optimization algorithms.When applied to Kapur multi-threshold segmentation,MIRBMO yields a threshold combination with higher entropy values and produces segmented images with superior peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),and feature similarity index measure(FSIM)values,indicating its strong application potential.
文摘针对单一传感器及单一蓝藻提取方法用于太湖蓝藻水华长时序监测的局限性,本文基于2014—2023年高分一号(GF-1)与Landsat 8多源影像数据,采用归一化植被指数(NDVI)方法、随机森林(RF)方法、基于最大类间方差确定样本(大津法)的随机森林(Otsu-RF)方法提取太湖蓝藻,通过对比分析确定蓝藻最优提取方法,揭示近10年太湖蓝藻水华的时空变化特征。结果表明:①Otsu-RF方法在不同影像下提取蓝藻水华的精度最高,且能够更有效地提取零星分布的蓝藻;②与GF-1图像相比,Landsat 8融合影像上的蓝藻像元纹理更加清晰,藻华提取结果更为精确;③2014—2023年太湖夏、秋季蓝藻水华爆发强度较高,春冬季较弱,其中2017、2020年太湖藻华爆发尤为严重,全域年平均蓝藻面积都超过了300 km 2;④太湖蓝藻水华春、夏、秋季多爆发在竺山湖湾、梅梁湖湾、西部湖区沿岸区域,冬季多发生在南部湖区沿岸区域。
