基于分割区域及特征相似度的玉米田遥感图像分类方法

A Corn Field of Remote Sensing Image Classification Method Based on Segmentation-Derived Regions and Feature Likeness

针对遥感图像中玉米田目标光谱复杂,同物异谱现象严重导致分类结果差的问题,提出一种基于分割区域及特征相似度的玉米田遥感图像分类方法。首先利用主成分分析法(PCA)对多光谱和高分辨全色融合图像进行第一主成分提取,以获得包含丰富图像信息的单色图像I;对I进行分水岭分割,得到一幅过分割目标区域图;构建由纹理、亮度及轮廓特征相似度组成的特征组;最后基于随机森林原理,利用构建的特征组对玉米目标进行提取。用高分一号卫星数据进行实验,并与支持向量机方法(SVM)、神经网络算法和最大似然算法进行了比较分析,实验表明,该方法的分类精度优于其他算法。

Corn field remote sensing images have a mass of endmember spectral variability and complexity, that results in the bad classification of planting area. A corn field of remote sensing image classification method based on segmentation-derived regions and feature likeness is proposed. First, principal component analysis （PCA） is used to extract the first principal component from the fusion image which is fused by the panchromatic and multi-spectral image, to acquire the monochromatic image I which contains rich information. Then, do a Watershed segmentation toI, we can get a graph of a split target area. Then build characteristic group which is composed of texture, brightness and contour feature likeness. At last Based on the principle of random forests, extract the corn target using the characteristic group. With the testing using GF-1 satellite remote sensing data and the results comparison analysis of the support vector machine （SVM）, neural network algorithm and maximum likelihood algorithm, it shows that the classification accuracy of this method is superior to other algorithms.