资源简介

摘要： 最先进的(state-of-the-art)机器学习遥感信息提取方法往往通过图像的波段组合、纹理分析构建特征向量，但是这种方法可选的特征有限且需要过多人为干预。通过建立卷积神经网络自动获取多波段遥感图像深层次的特征进行库布齐沙漠中绿地提取实验。训练分类器并进行超参数选择，通过交叉验证和对比分析来检验模型的性能。实验表明：建立的模型预测精度高，泛化能力强，为绿地以及更加复杂的地物信息提取开辟新的思路。 关键词： 卷积神经网络;  特征向量;  多波段遥感;  信息挖掘;  库布齐沙漠     Abstract: The key point of the state-of-the-art machine learning method to extract land information is to construct the features-vector.The existing methods mainly use the spectral features，texture features of remote sensing images to construct the features-vector，however，this method can only get limited features and requires too much human intervention.In the face of the above problems，this paper builds a convolutional neural network model for mining the deep-level features of multi-band remote sensing images and then extract the greenbelt in the Kubuqi Desert.The model was trained and hyperparameter selection was performed.The performance of the model was evaluated by cross validation and comparative analysis between methods.The experimental results show that the model is of high accuracy and good generalization ability.Finally，the test data set was input into the model to predict land cover classes and to do visualization.The importance of this study is to inspire new thinking of better performance of the green land and even more complex information extraction from remote sensing images.

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