基于支持向量机的Landsat-8影像森林类型识别研究

Identification of forest type with Landsat-8 image based on SVM

以吉林省汪清林业局天然林区为研究区,利用Landsat-8 OLI_TIRS多光谱遥感影像,结合森林资源野外调查数据,提取森林类型纹理、光谱特征参数,作为支持向量机的输入量,利用K-折交叉验证法确定最优核函数,识别森林类型,确定最优分类结果,评价分类精度,并与仅利用波段光谱特征的SVM分类结果进行精度对比。结果表明:利用纹理和光谱特征进行分类,构造SVM进行森林识别是可行的。惩罚系数C=100.0、核函数半径σ=1.000时的径向基核函数构造的支持向量机分类精度最好,总体分类精度可达89.58%,Kappa系数为0.87,单一分类精度中,阔叶林>针叶林>针阔混交林。只利用光谱特征的分类结果精度为81.26%,结合光谱和纹理特征的规律,能够提高分类精度。

In Wangqing natural forest area of Jilin Province, usingLandsat-80LI TIRS data,combined with the actual data of forest resources, took texture and spectral characteristic parameters as input element of support vector machine, chose kernel ftmction through k-fold cross-validation, identified forest types and determined the optimal classification, and compared with spectral characteristic classification accuracy. As the results showed： it is feasiblethat forest classification accuracy with texture and spectral characteristic based on SVM. Penalty coefficient C=100.0, kernel radius σ=1.000,radial basis kernel function of SVM classification accuracy was the highest, overall classification accuracy was 89.58%, Kappa coefficient was 0.87, classification accuracy of broad-leaved forest was better than coniferous and coniferous forest ,classification accuracy of the coniferous forest was the lowest. The accuracy of classification which only use the spectral characteristics was 81.26%. Forest classification accuracy with texture and spectral characteristic was better.