结合光照信息的多尺度K-均值植被几何光学四分量提取算法

Extracting Canopy Four Geometric-optical Components by Incorporating Illumination Information into a Multi-scale K-means Cluster Method

遥感几何光学模型利用传感器视场内植被、土壤组分随着光照与观测几何的变化模拟冠层多角度反射率。在近地遥感中,向下观测相机图像中的光照土壤、阴影土壤、光照植被和阴影植被这4种像元类型(称为植被几何光学四分量,简称四分量)是几何光学模型的重要输入参数。本文基于低成本成像传感器的RGB图像,提出一种结合光照信息和多尺度图像分层模型的K-均值四分量提取算法(MSI-Kmeans),首先通过综合利用颜色指数构成聚类空间,之后利用图像的光照分量和阴影分量子图分别构建多尺度图像分层模型,再在多尺度图像分层模型中进行K-均值聚类得到植被分量和土壤分量结果,最后将得到的四分量结果组合输出,从而实现植被几何光学四分量提取。对不同自然光照条件下获取的52幅多种植被冠层图像进行实验,结果表明:(1)在与多种常见算法的精度比较中,本文方法在四分量的分类中制图精度与用户精度均表现良好,综合评价Kappa系数最高(0.82);(2) MSI-Kmeans算法在不同冠层覆盖度和太阳高度角连续变化的条件下均可得到良好稳定的分类效果,具有应用于长期植被监测和单日内连续监测植被四分量变化的潜力。本算法的优势在于提高了阴影分量的分类精度和解决了高植被覆盖度下四分量的提取问题,后续的改进方向在于减小计算代价,提高算法在复杂场景下的适用性。本文研究成果对于利用数字相机进行植被几何光学四分量的提取和植被结构参数的估计具有较为重要的理论与应用价值。

The vegetation and soil fraction in the sensor's field of viewing will be varied with different light and observation geometry,and such variation can be used by the remote sensing geometric-optical model to simulate canopy multi-angle reflectance.As a result,the four components,i.e.,lit and shaded vegetation,as well as lit and shaded soil are important input parameters for the geometric-optical model.In this paper,an algorithm for extracting the four geometric-optical components with the combination of solar illumination information and multi-scale clusters derived from a k-means process was proposed.Firstly,the clustering space was formed by synthesizing a new color index,then the multi-scale image hierarchical model was constructed by using the lit and shaded component in the subgraphs of the images respectively,and then the k-means clustering was performed in the multi-scale image hierarchical model to obtain the vegetation component and soil component results.Finally,the obtained results in the above subgraphs were combined as the output to achieve the extraction of four geometric-optical components.Validation on the proposed method was conducted on fifty-two vegetation canopy images which were acquired under natural lighting conditions.We compared our results with those of OTSU threshold on ultra-green index,Fisher linear algorithm,and SHAR-LABFVC algorithm.The results showed that the proposed algorithm performed well in mapping accuracy and user accuracy in the classification of shaded components,and the highest Kappa coefficient(0.82)was achieved.Good and stable classification results were observed under the conditions of continuous changing canopy cover and solar altitude angle,and this promising result suggests that the proposed method has the potential in long-term vegetation monitoring as well as measuring vegetation four-component changes even in a single day.The advantages of this algorithm are to improve the classification accuracy of the shadow component and to solve the extraction problem of the four components under high vegetation coverage.However,reducing computational cost and thus to improve the applicability of this algorithm in complex scenes will need further efforts in the future work.