提出了一种基于线性混合模型的高光谱图像绿藻面积估计算法。利用端元提取算法, 自动获取图像中绿藻端元的光谱曲线, 根据得到的端元及原始图像, 通过全约束最小二乘算法, 求得绿藻端元的丰度图, 丰度图作为绿藻面积的估计结果。算法能够有效克服由于高光谱图像分辨率不足造成的绿藻面积估计不准确的问题, 实现亚像素水平的绿藻面积估计。利用2013年6月29日获取的GOCI传感器获取的8波段光谱图像展开实验, 计算得到当日绿藻覆盖面积为321 km2, 与HJ-1B卫星的实测结果高度接近, 相比于NDVI等传统算法具有明显优势。方法为绿藻灾害预警和监测提供了一条新的解决思路和技术途径, 具有较高的应用价值。

An green algae area estimation algorithm for hyperspectral image based on linear mixed model was proposed. According to the obtained endmembers and the original image, the abundance map of the green algae terminal was calculated by the fully constrained least squares algorithm, and the abundance map of green algae was regarded as the area estimation result directly. The algorithm can effectively overcome the problem of inaccurate estimation of the estimated area of green algae due to the lack of resolution of hyperspectral image, and realize the estimation of green algae area at sub-pixel level. Based on the Geostationary Ocean Color Imager (GOCI) 8 bands image unfolding experiment on June 29, 2013, the estimated coverage of green algae was 321 km2, which was close to that of HJ-1B satellite. Compared with NDVI and other traditional algorithms, the proposed method has obvious advantages. Traditional methods usually present higher estimation results, because they could only justify whether a pixel includes green algae or not. The proposed method may provide a new way of thinking and technology for early warning and monitoring of green algae, and has a high application value.