结合地形因子与分层策略的喀斯特地区无人机影像分类

Unmanned image classification in karst area combining topographic factors and stratification strategy

西南喀斯特山区地形起伏较大,地物分布较为破碎,致使传统的光谱特征一次分类方法的精度较低。本文基于高分辨率无人机正射影像和地形指标,充分利用无人机遥感影像空间特征、光谱特征、纹理特征及地形特征,采取面向对象CART决策树算法与分层策略提取了研究区土地覆盖类型。研究表明,结合空间地形因子和分层策略的方法减少了破碎区地物间的相干扰,故具有较高的分类精度,总体分类精度达91.2%,Kappa系数为0.87,较传统一次分类精度提高了9.8%,Kappa系数提高了0.13。该方法对西南喀斯特地区土地覆盖解译精度较好,可为土地利用监测提供参考。

The karst topography in southwestern China is filled with fantastic caves and curved areas. And the classification of groundobject is relatively fragmented, which leads to the reductionin in the accuracy of the traditional one-time classification of spectral features.Based on the high-definition of UAV orthophoto and terrain factor, this paper makes full use of the spatial, spectral, texture and terrain features of UAV remote sensing images and adopts the object-oriented CART decision tree algorithm and hierarchical strategy to pickup the coverage type of the land inspecific study area. The method of combining spatial terrain factors and hierarchical strategies reduces the interference among the fragment edground objects in the karst area actually has a much higher classification accuracy. The whole classification accuracy reach 91.2%and the Kappa coefficient is 0.87, which is respectively 9.8% and 0.13 higher than the traditional one-time classification accuracy and Kappa coefficient. It shows that this method is suitable for decoding land coverage in southwest karst area, which could provide favorable reference for land-use monitoring.