面向对象土地覆被图像组合分类方法

Combinational approach of object oriented land cover image classification

研究了支持向量机在面向对象土地覆被图像分类中的应用技术,提出采用最小二乘支持向量机(LSSVM)与模糊灰色关联度联合评估(FG)相结合的一种新的组合分类方法简记FG-LSSVM,为土地覆被分类提供一种可行的高精度分类途径。根据图像上不同对象的空间尺度和光谱值特征,基于稳健的核密度梯度分割算法提取具有任意形状和唯一标识的均质对象后,为了比较提出方法的性能,采用原始对象样本依次验证了3个面向对象分类方法,即标准支持向量机方法、以模糊贴近度作为模糊因子的模糊支持向量机方法和传统K最近邻面向对象分类方法。实现了一个高精度面向对象土地覆被图像分类信息系统。试验结果表明:提出的FG-LSSVM面向对象方法相比标准支持向量机、模糊支持向量机与K最近邻方法试验精度约提高2.4%左右。提出的方法在识别效果上,符合研究区实际分类应用的要求。

Applied technique of object-based land cover image classification for support vector machines were studied.And a combinational approach was estiblished,namely FG-LSSVM,with least squares support vector machines（LSSVM）and fuzzy and grey degree of correlation（FG）,which was a feasible high-precision image classification algorithm for land cover.According to the spatial scale and spectral characteristics of different targets on rectified image,the number of objects was automatically determined by using the stable gradient of kernel density algorithm,in which local objects with unique identifier in arbitrary shapes were picked up.To compare the performance of the presented method with that of other object oriented methods,with original samples,three models were successively verified,which were standard support vector machines（SVM）and the fuzzy nearness improved support vector machines（FSVM）,and the traditional K nearest neighbor（KNN）object-oriented methods.A high precision land cover image classification system was established with the proposed approach.The results showed the total precision of FG-LSSVM was about 2.4%higher than that of SVM,FSVM and KNN object-oriented methods in the study area.The proposed method also meets the requirements of land cover image classification in respect of efficiency and effects.