论文部分内容阅读
本文为了提高地物识别的正确性,克服异物同谱和同物异谱现象,以渭干河?库车河三角洲绿洲为例,利用ETM+数据,探讨了该绿洲盐渍化土地覆盖信息的提取方法。文章提出了基于SVM的光谱和纹理两种信息复合的分类方法,通过此方法对该绿洲进行分类研究,并将分类结果与最小距离法、最大似然法(MLC)和单源数据(光谱)SVM分类结果进行定性和定量比较分析。研究结果表明:该方法能够有效地解决单数据源分类效果破碎、分类精度不高等问题,并对高纬输入向量具有较高的推广能力,因此该方法更适合于遥感图像分类和盐渍化信息提取,是地物遥感信息提取的有效途径。
In order to improve the correctness of object recognition and overcome the phenomenon of the same spectrum and same-spectrum phenomenon of the foreign object, taking the Weigan River and Kuqa River Delta as an example, ETM + data were used to investigate the extraction of the salinized land cover information in this oasis method. In this paper, a classification method based on SVM is put forward, which can be used to classify the oasis. The classification results are compared with the minimum distance method, maximum likelihood method (MLC) and single source data (spectrum) SVM classification results were qualitative and quantitative comparative analysis. The results show that this method can effectively solve the problem of single data source classification effect is not high, the classification accuracy is not high, and high popularization ability of high-latitude input vector, so this method is more suitable for remote sensing image classification and salinization information Extraction is an effective way to extract remote sensing information of land object.