图像滤波算子在土壤盐渍化信息提取中的应用

Application of Image Filtering Operator in Extraction of Soil Salinization Information

针对由噪声引起的遥感影像质量下降,选用7种典型的滤波算子分别对遥感影像进行处理,并结合支持向量机(SVM)的分类方法,分析滤波后影像亮度值的变化,与未经过滤波处理的影像进行分类后精度的对比。结果表明:相对于未经处理的遥感影像,经过滤波处理后的影像在土壤盐渍化信息提取中具有较高的分类精度;其中的高斯高通滤波结合SVM的土壤盐分提取模型的分类精度和Kappa系数由86.7285%和82.21%分别提高到89.6950%和86.20%,其分类效果最佳。滤波运算能抑制噪声、提高影像质量,能有效提高方法的盐渍化监测能力。掌握土壤盐渍化的空间分布特征及时空变化规律,对干旱区及半干旱区土壤盐渍化的防治和缓解、保护脆弱的生态环境都具有现实意义。

To reduce the noise in remote-sensing images,seven typical filtering operators are selected to separately process the remote-sensing images.Combined with the classification method of support vector machine(SVM),we analyze the variation of images’brightness values after filtering and compare their accuracy with that of unfiltered remote-sensing images.The results show that the filtered remote-sensing images have a higher classification accuracy for the extraction of soil salinization compared with untreated remote-sensing images.Of the several selected filtering operators,the soil-salinity extraction model that uses Gaussian low-pass filtering and SVM can improve the classification accuracy and the Kappa coefficient from 86.7285%and 82.21%to 89.6950%and 86.20%,respectively,which is the best classification accuracy to date.To summarize,the filtering operation suppresses noise,improves image quality,effectively improves the monitoring ability of salinization.Grasping the spatial distribution characteristics and temporal and spatial variation principle of soil salinization is of practical significance for preventing and mitigating soil salinization to protect fragile ecological environments in arid and semi-arid regions.