收稿：2021-05-07，
纸质出版：2023-07-07
移动端阅览
为了更加准确地利用ASTER影像辅助填图，提出了一种结合小波变换、支持向量机（SVM）和投票法的ASTER影像岩性自动分类方法。首先，采用Haar小波对ASTER影像进行多尺度小波分解，统计小波系数的均值作为纹理特征，同时提取灰度共生矩阵（GLCM）方差、同质性、均值纹理特征；然后，利用小波纹理、GLCM纹理及光谱特征构造SVM分类的特征向量，并进行10次重复分类；最后利用投票法确定岩性单元。对结果进行统计评估，结合多种纹理，并利用投票法得到的岩性分类精度为92.1934%，Kappa系数为0.9202，比仅用光谱分类精度提高了13.3369%。小波纹理能提取更细节的岩性信息；投票法可以避免岩性因样本的空间变异性产生的动态变化，优化分类结果；SVM较最大似然法（MLC）更适合于训练数据集高维且非正态分布的岩性分类；采用人工蜂群算法搜索SVM的最优参数，可避免参数局部最优。
In ASTER images
different lithologic units show obvious multiscale texture features
and wavelet transform has the advantage of extracting multiscale features. Support Vector Machine (SVM) is suitable for solving the classification problem of little training data and nonnormal data distribution. SVM is used to complete lithology classification. The classification results have high classification accuracy and low uncertainty. Using the voting method in selecting lithologic classification results can avoid the uncertainty of lithologic classification results caused by the extraction method of lithologic samples
thereby making the classification results statistically significant. An automatic classification method for ASTER image lithology integrating the wavelet texture
SVM
and voting method is proposed to improve the accuracy of ASTER imagery exploited for mapping assistance. First
the Haar wavelet is utilized for decomposing the ASTER image involving a multiscale wavelet
with the mean value of wavelet coefficients considered texture features. Moreover
the variance
homogeneity
and mean values of the gray-level co-occurrence matrix (GLCM) are extracted concurrently. Then
the feature vectors of the SVM classification are constructed with multiscale texture
GLCM texture
and spectral features. The classification is repeated 10 times. Finally
the lithologic unit is determined by the voting method
and the results are statistically evaluated. The lithologic classification involves 92.1934% accuracy
exceeding the accuracy of spectral classification by 13.3369%
with a kappa coefficient of 0.9202. The multiscale texture extracts detailed lithologic information. The voting method prevents the dynamic lithologic change caused by the spatial variability of samples. The SVM also demonstrates superiority over the maximum likelihood classifier for lithologic classification involving high-dimensional and nonnormal distribution data. The local optimal parameters of SVM are avoided using the artificial bee colony algorithm to search for optimal parameters.
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