【作者】 刘伟；

【导师】 汪传建； 赵庆展；

【作者基本信息】 石河子大学 ， 农业信息化技术及应用， 2017， 硕士

【摘要】 遥感影像的获取与分类技术是遥感监测过程中的基础和关键。以往的航天航空遥感虽然能快速获取大面积遥感影像,但是空间、时间分辨率较低。随着当前无人机和轻小型传感器的发展,使高空间、时间分辨率的低空影像的获取成为可能。当前多光谱影像在应用中易产生“同谱异物”、“同物异谱”等现象,更高的光谱分辨率也让相邻波段间的相关性大大增加,使得计算复杂度和时间复杂度大幅提高。因此对多(高)光谱遥感影像数据进行降维处理是目前应用研究中的一个难点问题。本文面向低空多光谱地物分类,基于最佳波段指数和影像的光谱特征、纹理特征进行最佳波段组合的选择,然后利用支持向量机和最小二乘支持向量机分类方法构建了多组分类模型进行分类对比实验。主要工作及相关研究成果如下:(1)利用大型固定翼无人机搭载轻型多光谱相机搭建无人机遥感影像采集平台并获取了地面分辨率为22.6cm的12波段无人机多光谱遥感影像,再通过Pix4D Mapper对原始图像进行基于特征的配准和特征级的融合得到研究区域的正射影像。(2)针对无人机多光谱影像数据的空间分辨率高、波段间相关性大等特点,综合影像的植被及水体指数等光谱信息,影像主成分分析和灰度共生矩阵计算得到的纹理特征信息及最佳波段指数法筛选的原始波段得到了最佳波段组合来进行地物分类。(3)针对研究区初始得到的波段组合,设计监督分类和非监督分类的对比实验。相对原始波段组合,其中研究区域A的1,6,11,NDVI,NDWI,Mean波段组合的IsoData分类精度从83.57%提高到89.80%,SVM分类精度95.58%提高到99.76%。实验证明此波段组合不仅包含较多的波段信息且波段间相关性系数较低,同时反映了地物的光谱信息和纹理信息,可选择其作为Micro MCA12 Snap的最佳波段组合。(4)针对实验得到的最佳波段组合,分别使用粒子群优化和网格搜索算法进行参数寻优并使用交叉验证的方法对研究区域进行SVM和LSSVM对比实验。实验结果表明,以粒子群优化进行参数寻优得到的LSSVM分类模型,相对SVM粒子群优化分类精度从97.833%提高到99.854%;相对LSSVM网格搜索分类精度从99.762%提高到99.854%。同时LSSVM粒子群优化在一定程度上提高了分类的速度,是针对本文最佳波段组合在地物分类上的理想分类模型。更多还原

【Abstract】 Acquisition and classification of remote sensing image is the basis and key technology in the process of remote sensing monitoring.Different from the traditional aerospace remote sensing of low spatial resolution,temporal resolution and vulnerable to the effects of atmospheric environment,rapid development of unmanned aerial vehicle(UAV)and lightweight sensors brings out the possibility of low altitude remote sensing and high spatial resolution,high spectral resolution image.At the same time multi-spectral images tend to produce "same-spectrum foreign body","foreign body with the spectrum" and so on;which has brought difficulties to follow-up’s treatment.Higher spectral resolution also increases the correlation between adjacent bands,and consequently a large amount of information redundancy,which not only brings computational complexity but also increases the time complexity.Therefore,it is a difficult problem in the application research to reduce the dimension processing of multi(high)spectral remote sensing data.Oriented low-altitude multi-spectral features classification,and then based on the best band index and the spectral characteristics,texture feature of image to choose the best combination of bands.Finally,the support vector machine and the least squares support vector machine(SVM)are used to build the classification model for classification and comparison experiments.The main work and related research results are as follows:(1)The remote sensing image acquisition platform of UAV was built by large-scale fixed wing unmanned aerial vehicle,which equipped with light multi-spectral camera,and then the remote sensing images of UAV with 22.6cm GSD and 12 bands were obtained.Then the original image was registered and fused at the characteristic level to obtain the orthographic image of the study area by Pix4 D Mapper.(2)Aiming at the characteristics of high spatial resolution and high inter-band correlation of UAV multi-spectral image data,synthesize spectral information such as vegetation and water related index,texture feature information acquired by PCA and GLCM,the original bands which was filtered by the best band index method to obtain the best band combination for feature classification.(3)The unsupervised and supervised classification methods were designed to classify the objects in the study area.Compared to the original band combination,the accuracy of Iso Data classification of the 1,6,11,NDVI,NDWI and Mean bands in the study area A increased from 83.57% to 89.80% and the SVM classification accuracy was increased from 95.58% to 99.76%.Experiment results confirm that the best bands combination not only has more band information and the inter-bands correlation coefficient is lower,but also reflects the spectral information and texture information of objects,so it can be selected as the best bands combination for the Micro MCA12 Snap.(4)According to the best bands combination from the experiment,SVM and LSSVM experiments were carried out on the study area by using particle swarm optimization and grid search algorithm for parameter optimization and cross validation.The LSSVM with PSO classification model is obtained by parameter optimization of particle swarm optimization.Compared with the SVM particle swarm optimization,the classification accuracy improved from 97.833% to 99.854%;Compared with the LSSVM grid search,the classification accuracy improved from 99.762% to 99.854%.At the same time,LSSVM particle swarm optimization improves the classification speed to a certain extent,and is an ideal classification model for the classification of the best bands combination in this paper.更多还原