自然资源遥感2025，Vol.37Issue(2)：88-95,8.DOI:10.6046/zrzyyg.2023349
基于字典学习光谱解混的绿藻亚像元面积估计
Subpixel-level area estimation of green algae based on spectral unmixing in dictionary learning
摘要
Abstract
Green tides have emerged as a significant marine ecological disaster worldwide,rendering the accurate detection and area estimation of green algae crucial.To accurately estimate the coverage area of green algae communities in the monitoring of green tides based on low-resolution satellite images,this study proposed a dictionary learning-based method for estimating the area of green algae using hyperspectral images.The proposed method involves deriving the endmember spectrum database that is closest to the unknown surface feature spectra via online robust dictionary learning,obtaining the abundance map of green algae through sparse coding,and calculating the coverage area of green algae.It was verified through the experiment using the spectral images acquired by the geostationary ocean color imager(GOCI)on June 25,2016,and June 21,2020.The experimental results reveal that the calculated coverage areas of green algae on the two days were highly close to the approximate measured results,with a minimum error of only 2.15％,suggesting that the proposed method outperforms traditional index-based hard thresholding algorithms.Independent of the pure pixel hypothesis,the proposed method can effectively address the mixed pixel problem and enhance area estimation accuracy in the absence of a pre-estimated number of endmembers or prior spectral information,thereby achieving high-precision subpixel-level area estimation of green algae.关键词
高光谱图像/稀疏解混/绿藻检测/面积估计/地物提取Key words
hyperspectral image/sparse unmixing/detection of green algae/area estimation/surface feature ex-traction分类
信息技术与安全科学引用本文复制引用
张贻然,潘斌,徐夏,朱俊峰..基于字典学习光谱解混的绿藻亚像元面积估计[J].自然资源遥感,2025,37(2):88-95,8.基金项目
国家自然科学基金项目"基于多目标优化的高光谱遥感图像稀疏解混研究"(编号:62001251)、"多层级域适应高光谱遥感图像分类"(编号:62001252)和中央高校基本科研业务费专项资金(编号:63243074)共同资助. （编号:62001251）
