基于卷积神经网络的青海湖区域遥感影像分类

科学准确的获取青海湖区域土地覆盖分类对于研究该区域生态环境变化有着重要的意义.本文使用30米分辨率的LandSat 8 OLI青海湖区域遥感影像数据展开相关研究，30米分辨率属于中等分辨率，当前中分遥感影像的分类方法尚存在特征提取困难、分类精度不高等问题.本文借鉴GoogLeNet Inception结构，设计并提出了一种卷积神经网络模型进行特征提取及分类，分析了用于样本生成的邻域窗口尺寸对分类结果的影响，并与最大似然分类和SVM分类方法进行比较.结果表明，在窗口尺寸为9×9时，CNN模型的总体分类效果最好，且CNN的分类结果明显优于最大似然分类方法和SVM.

Classification of Remote Sensing Images in Qinghai Lake Based on Convolutional Neural Network

Scientific and accurate access to the classification of land cover in Qinghai Lake area is of great significance to the study of the ecological environment changes in this region. In this study, we use the 30 meter resolution LandSat 8 OLI remote sensing image data of Qinghai Lake to carry out the related research. The 30 m resolution is of medium resolution. The methods for classification of medium resolution remote sensing image still have defects of difficult feature extraction and low classification accuracy. In this study, using the GoogLeNet inception structure, a Convolutional Neural Network (CNN) model for feature extraction and classification is designed and proposed. We analyzed the effect of the neighborhood window size used for sample generation on the classification results, and compared it with the maximum likelihood classification and SVM classification method. The results show that when the window size is 9×9, the overall classification effect of the CNN model is the best, and the classification results of CNN are obviously better than that of maximum likelihood classification and SVM.